• Zhang, F., Zhang, Y., Casanovas, P., Schattschneider, J., Walker, S. P., Xue, B., . . . Symonds, J. E. (2025) "Health prediction for king salmon via evolutionary machine learning with genetic programming". Journal of the Royal Society of New Zealand, 55(1), 166-191. doi:10.1080/03036758.2024.2329228
  • Wang, C., Behrens, E., Ma, H., Chen, G., & Huang, V. (2025) "Climate Downscaling Monthly Coastal Sea Surface Temperature Using Convolutional Neural Network and Composite Loss". In Unknown Book (Vol. 15442 LNAI, pp. 303-315). doi:10.1007/978-981-96-0348-0_22
  • Sun, Y., Gomes, H. M., Pfahringer, B., & Bifet, A. (2025) "Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis". In Unknown Book (Vol. 15285 LNAI, pp. 91-97). doi:10.1007/978-981-96-0128-8_8
  • Rimas, M., Chen, Q., & Zhang, M. (2025) "Importance-Based Pruning for Genetic Programming Based Symbolic Regression". In Unknown Book (Vol. 15443 LNAI, pp. 188-201). doi:10.1007/978-981-96-0351-0_14
  • Rezayat, S., Burmester, G., Ma, H., & Hartmann, S. (2025) "Conceptual Framework for Designing Hippocratic APIs". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 15238 LNCS (pp. 355-376). doi:10.1007/978-3-031-75872-0_19
  • Fang, Z., Ma, H., Chen, G., Hartmann, S., & Wang, C. (2025) "Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment". In Unknown Book (Vol. 15442 LNAI, pp. 86-97). doi:10.1007/978-981-96-0348-0_7
  • Chen, R., Mei, Y., Zhang, F., & Zhang, M. (2025) "Neural Network Surrogate Based on Binary Classification for Assisting Genetic Programming in Searching Scheduling Heuristic". In Unknown Book (Vol. 15281 LNAI, pp. 309-321). doi:10.1007/978-981-96-0116-5_25
  • Botacin, M., & Gomes, H. (2025) "Towards more realistic evaluations: The impact of label delays in malware detection pipelines". Computers and Security, 148. doi:10.1016/j.cose.2024.104122
  • Santolin, J., Larsen, O. C., Fritze, A., Xue, B., Yang, Z., & Rotter, V. S. (2024) "Reaching China’s fertilizer reduction goals through nitrogen and phosphorus recovery: a substance flow analysis case study". Journal of Material Cycles and Waste Management, 26(6), 3650-3664. doi:10.1007/s10163-024-02067-6
  • Wan, X., Zuo, X., Lu, T., & Chen, G. (2024) "A hybrid niching memetic algorithm for multi-modal optimization of double row layout problem". International Journal of Production Research.
  • Yu, W., Yang, J., Sun, D., Ren, J., Xue, B., Sun, W., . . . Li, X. (2024) "How urban heat island magnifies hot day exposure: Global unevenness derived from differences in built landscape". Science of the Total Environment, 945. doi:10.1016/j.scitotenv.2024.174043
  • Botacin, M., & Gomes, H. (2024) "Cross-Regional Malware Detection via Model Distilling and Federated Learning". In ACM International Conference Proceeding Series (pp. 97-113). doi:10.1145/3678890.3678893
  • Yang, Y., Chen, G., Ma, H., Hartmann, S., & Zhang, M. (2024) "Enhancing generalization in genetic programming hyper-heuristics through mini-batch sampling strategies for dynamic workflow scheduling". Information Sciences, 678. doi:10.1016/j.ins.2024.120975
  • Qu, H., Wang, G. G., Li, Y., Qi, X., & Zhang, M. (2024) "ConvFishNet: An efficient backbone for fish classification from composited underwater images". Information Sciences, 679. doi:10.1016/j.ins.2024.121078
  • Peng, Y., Chen, G., Zhang, M., & Xue, B. (2024) "Proximal evolutionary strategy: improving deep reinforcement learning through evolutionary policy optimization". Memetic Computing, 16(3), 445-466. doi:10.1007/s12293-024-00419-1
  • Sun, Y., Gomes, H. M., Pfahringer, B., & Bifet, A. (2024) "Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis". __. doi:10.48550/arxiv.2408.16187
  • Gomes, H. M., & Bifet, A. (2024) "Practical Machine Learning for Streaming Data". In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 6418-6419). doi:10.1145/3637528.3671442
  • Zhao, J., Xue, B., & Zhang, M. (2024) "SGformer: Boosting transformers for indoor lighting estimation from a single image". Computational Visual Media, 10(4), 671-686. doi:10.1007/s41095-024-0447-8
  • Pham, D., Tran, B., Nguyen, S., Alahakoon, D., & Zhang, M. (2024) "Fairness optimisation with multi-objective swarms for explainable classifiers on data streams". Complex and Intelligent Systems, 10(4), 4741-4754. doi:10.1007/s40747-024-01347-w
  • Zhang, T., Bi, Y., Liang, J., Xue, B., & Zhang, M. (2024) "Decomposition-based Multi-objective Genetic Programming for Feature Learning in Image Classification". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 555-558). doi:10.1145/3638530.3654306
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2024) "Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 998-1006). doi:10.1145/3638529.3654075
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2024) "A Semantic-based Hoist Mutation Operator for Evolutionary Feature Construction in Regression [Hot off the Press]". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 65-66). doi:10.1145/3638530.3664071
  • Yan, L. F., Ma, H., & Chen, G. (2024) "Reinforcement Learning-Assisted Genetic Programming Hyper Heuristic Approach to Location-Aware Dynamic Online Application Deployment in Clouds". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 988-997). doi:10.1145/3638529.3654058
  • Xue, B., & Zhang, M. (2024) "Evolutionary Computation for Feature Selection and Feature Construction". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 1158-1186). doi:10.1145/3638530.3648411
  • Wei, J., Xue, B., & Zhang, M. (2024) "EZUAS: Evolutionary Zero-shot U-shape Architecture Search for Medical Image Segmentation". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 422-430). doi:10.1145/3638529.3654041
  • Wang, S., Mei, Y., & Zhang, M. (2024) "A Preliminary Counterfactual Explanation Method for Genetic Programming-Evolved Rules: A Case Study on Uncertain Capacitated Arc Routing Problem". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 547-550). doi:10.1145/3638530.3654192
  • Wang, C., Chen, Q., Xue, B., & Zhang, M. (2024) "Multi-task Genetic Programming with Semantic based Crossover for Multi-output Regression". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 543-546). doi:10.1145/3638530.3654282
  • Ul Ain, Q., Al-Sahaf, H., Xue, B., & Zhang, M. (2024) "Feature Extraction with Automated Scale Selection in Skin Cancer Image Classification: A Genetic Programming Approach". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 1363-1372). doi:10.1145/3638529.3654071
  • Pei, J., Liu, J., & Mei, Y. (2024) "Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 1017-1025). doi:10.1145/3638529.3654062
  • Nguyen, S., Thiruvady, D., Sun, Y., & Zhang, M. (2024) "Genetic-based Constraint Programming for Resource Constrained Job Scheduling". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 942-951). doi:10.1145/3638529.3654046
  • Mei, Y., & Raidl, G. (2024) "Evolutionary Computation meets Machine Learning for Combinatorial Optimisation". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 1328-1351). doi:10.1145/3638530.3648413
  • Londt, T., Gao, X., & Mei, Y. (2024) "A Cooperative Coevolution Neural Architecture Search Approach for Evolving Convolutional Neural Networks". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 599-602). doi:10.1145/3638530.3654102
  • Liu, Y., Cui, Y., Cheng, W., Browne, W. N., Xue, B., Zhu, C., . . . Zeng, L. (2024) "A Phenotypic Learning Classifier System for Problems with Continuous Features". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 349-357). doi:10.1145/3638529.3654007
  • Liao, X. C., Mei, Y., & Zhang, M. (2024) "Learning Traffic Signal Control via Genetic Programming". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 924-932). doi:10.1145/3638529.3654037
  • Li, Q., Gao, X., Meng, W., & Ma, J. (2024) "Multi-objective multi-population Genetic Programming for feature selection and classification to high-dimensional data". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 519-522). doi:10.1145/3638530.3654234
  • Jia, K., Gao, X., Niu, J., & Ma, J. (2024) "Archive-based multiple feature construction methods using adaptive Genetic Programming". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 503-506). doi:10.1145/3638530.3654239
  • Falloon, M., Ma, H., & Chen, A. (2024) "Energy-Aware Dynamic Resource Allocation and Container Migration in Cloud Servers: A Co-evolution GPHH Approach". In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 1219-1227). doi:10.1145/3638529.3654070
  • Demir, K., Nguyen, B., Xue, B., & Zhang, M. (2024) "Evolutionary Sparse Coding and Graph Regularisation for Embedded Multi-label Feature Selection". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 271-274). doi:10.1145/3638530.3654094
  • Andersen, H., Lensen, A., Browne, W., & Mei, Y. (2024) "Intepretable Local Explanations Through Genetic Programming". In GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (pp. 247-250). doi:10.1145/3638530.3654370
  • Xue, B., Xu, Y., & Yang, J. (2024) "Remote Sensing of the Interaction between Human and Natural Ecosystems in Asia". Remote Sensing, 16(13). doi:10.3390/rs16132255
  • Wang, F., Zuo, X., Huang, H., & Chen, G. (2024) "ADBA: Approximation Decision Boundary Approach for Black-Box Adversarial Attacks". Retrieved from https://arxiv.org/abs/2406.
  • Zong, F., Wang, L., Liu, H., Xue, B., Bai, R., & Liu, Y. (2024) "A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion–relaxation correlation MRI". Computers in Biology and Medicine, 175. doi:10.1016/j.compbiomed.2024.108508
  • Zhao, H., Zhang, C., Xue, B., Zhang, M., & Zhang, B. (2024) "A Two-Stage Differential Evolutionary Algorithm for Deep Ensemble Model Generation". IEEE Transactions on Evolutionary Computation, 28(3), 810-824. doi:10.1109/TEVC.2022.3231387
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2024) "A geometric semantic macro-crossover operator for evolutionary feature construction in regression". Genetic Programming and Evolvable Machines, 25(1). doi:10.1007/s10710-023-09465-z
  • Zhai, R., Mei, Y., Guo, T., & Du, W. (2024) "A Collaborative Drone-Truck Delivery System With Memetic Computing Optimization". IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(6), 3618-3630. doi:10.1109/TSMC.2024.3371471
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2024) "Guest Editorial Evolutionary Neural Architecture Search". IEEE Transactions on Evolutionary Computation, 28(3), 566-569. doi:10.1109/TEVC.2024.3388280
  • Mostofi, A., Jain, V., Kumar, S., Mei, Y., & Chandra, C. (2024) "RETRACTED ARTICLE: A game theory data science-based mechanism for licensed pharmaceutical products concerning their deterioration: a case of a micro, small, and medium enterprise in Iran(Annals of Operations Research, (2024), 337, (S23–S24))". Annals of Operations Research, 337(Suppl 1), 23-24. doi:10.1007/s10479-023-05360-z
  • Meng, W., Li, Y., Gao, X., & Ma, J. (2024) "Ensemble classifiers using multi-objective Genetic Programming for unbalanced data". Applied Soft Computing, 158. doi:10.1016/j.asoc.2024.111554
  • Huang, Z., Mei, Y., Zhang, F., Zhang, M., & Banzhaf, W. (2024) "Bridging directed acyclic graphs to linear representations in linear genetic programming: a case study of dynamic scheduling". Genetic Programming and Evolvable Machines, 25(1). doi:10.1007/s10710-023-09478-8
  • Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2024) "A Global and Local Surrogate-Assisted Genetic Programming Approach to Image Classification". IEEE Transactions on Evolutionary Computation, 28(3), 718-732. doi:10.1109/TEVC.2022.3214607
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2024) "Genetic Programming for Feature Selection Based on Feature Removal Impact in High-Dimensional Symbolic Regression". IEEE Transactions on Emerging Topics in Computational Intelligence, 8(3), 2269-2282. doi:10.1109/TETCI.2024.3369407
  • Lin, B., Mei, Y., & Zhang, M. (2024) "Automated Design of State Transition Rules in Ant Colony Optimization by Genetic Programming: A Comprehensive Investigation  ". __. doi:10.21203/rs.3.rs-4445728/v1
  • Huang, Z., Mei, Y., Zhang, F., Zhang, M., & Banzhaf, W. (2024) "Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations". Retrieved from http://arxiv.org/abs/2405.
  • Luna, R., Cassales, G., Pfahringer, B., Bifet, A., Gomes, H. M., & Senger, H. (2024) "Mini-batching with Fused Training and Testing for Data Streams Processing on the Edge". In Proceedings of the 21st ACM International Conference on Computing Frontiers, CF 2024 (pp. 51-60). doi:10.1145/3649153.3649188
  • Yuan, G., Xue, B., & Zhang, M. (2024) "An evolutionary neural architecture search method based on performance prediction and weight inheritance". Information Sciences, 667. doi:10.1016/j.ins.2024.120466
  • Yang, C., Xue, B., Tan, K. C., & Zhang, M. (2024) "A Co-Training Framework for Heterogeneous Heuristic Domain Adaptation". IEEE Transactions on Neural Networks and Learning Systems, 35(5), 6863-6877. doi:10.1109/TNNLS.2022.3212924
  • Xue, B., Xu, Y., Yang, J., & Xiao, X. (2024) "Applications of Machine Learning in National Territory Spatial Planning". Applied Sciences (Switzerland), 14(10). doi:10.3390/app14104045
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (2024) "Genetic Programming and Reinforcement Learning on Learning Heuristics for Dynamic Scheduling: A Preliminary Comparison". IEEE Computational Intelligence Magazine, 19(2), 18-33. doi:10.1109/MCI.2024.3363970
  • Huang, V., Chen, G., Zuo, X., Zomaya, A. Y., Sohrabi, N., Tari, Z., & Fu, Q. (2024) "Request Dispatching Over Distributed SDN Control Plane: A Multiagent Approach". IEEE Transactions on Cybernetics, 54(5), 3211-3224. doi:10.1109/TCYB.2023.3266448
  • Gunasekara, N., Pfahringer, B., Gomes, H., & Bifet, A. (2024) "Gradient boosted trees for evolving data streams". Machine Learning, 113(5), 3325-3352. doi:10.1007/s10994-024-06517-y
  • Bifet, A., Gama, J., Gomes, H. M., & Veloso, B. (2024) "Special Track on Data Streams". Proceedings of the ACM Symposium on Applied Computing, 350.
  • Pei, W., Xue, B., Zhang, M., Shang, L., Yao, X., & Zhang, Q. (2024) "A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?". IEEE Transactions on Evolutionary Computation, 28(2), 353-373. doi:10.1109/TEVC.2023.3257230
  • Bi, Y., Xue, B., & Zhang, M. (2024) "Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification". IEEE Transactions on Evolutionary Computation, 28(2), 307-322. doi:10.1109/TEVC.2022.3214503
  • Cravens, B., Lensen, A., Maddigan, P., & Xue, B. (2024) "Genetic Programming Trees for Explainable Manifold Learning". __. doi:10.48550/arXiv.2403.14139
  • Ceschin, F., Botacin, M., Bifet, A., Pfahringer, B., Oliveira, L. S., Gomes, H. M., & Grégio, A. (2024) "Machine Learning (In) Security: A Stream of Problems". Digital Threats: Research and Practice, 5(1). doi:10.1145/3617897
  • Maddigan, P., Lensen, A., & Xue, B. (2024) "Explaining Genetic Programming Trees using Large Language Models". __. doi:10.48550/arXiv.2403.03397
  • Xie, X., Sun, Y., Liu, Y., Zhang, M., & Tan, K. C. (2024) "Architecture Augmentation for Performance Predictor via Graph Isomorphism". IEEE Transactions on Cybernetics, 54(3), 1828-1840. doi:10.1109/TCYB.2023.3267109
  • Li, N., Ma, L., Yu, G., Xue, B., Zhang, M., & Jin, Y. (2024) "Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications, and Open Issues". ACM Computing Surveys, 56(2). doi:10.1145/3603704
  • Zheng, Y., Zhang, F., Gao, X., & Ma, J. (2024) "Bagging-based ensemble classifiers using multi-objective Genetic Programming". __. doi:10.21203/rs.3.rs-3948541/v1
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2024) "Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling". IEEE Transactions on Evolutionary Computation, 28(1), 147-167. doi:10.1109/TEVC.2023.3255246
  • Wu, B., Zuo, X., Chen, G., Ai, G., & Wan, X. (2024) "Multi-agent deep reinforcement learning based real-time planning approach for responsive customized bus routes". Computers and Industrial Engineering, 188. doi:10.1016/j.cie.2023.109840
  • Nguyen, S., Thiruvady, D., Sun, Y., & Zhang, M. (2024) "Genetic-based Constraint Programming for Resource Constrained Job Scheduling". __. doi:10.48550/arxiv.2402.00459
  • Nguyen, B. H., Xue, B., & Zhang, M. (2024) "A Constrained Competitive Swarm Optimizer With an SVM-Based Surrogate Model for Feature Selection". IEEE Transactions on Evolutionary Computation, 28(1), 2-16. doi:10.1109/TEVC.2022.3197427
  • Huang, Z., Mei, Y., & Zhong, J. (2024) "Semantic Linear Genetic Programming for Symbolic Regression". IEEE Transactions on Cybernetics, 54(2), 1321-1334. doi:10.1109/TCYB.2022.3181461
  • Fu, W., Xue, B., Gao, X., & Zhang, M. (2024) "Genetic Programming for Document Classification: A Transductive Transfer Learning System". IEEE Transactions on Cybernetics, 54(2), 1119-1132. doi:10.1109/TCYB.2023.3338266
  • Eisenbarth, H., Oxner, M., Shehu, H. A., Gastrell, T., Walsh, A., Browne, W. N., & Xue, B. (2024) "Emotional arousal pattern (EMAP): A new database for modeling momentary subjective and psychophysiological responding to affective stimuli". Psychophysiology, 61(2). doi:10.1111/psyp.14446
  • Demir, K., Nguyen, B. H., Xue, B., & Zhang, M. (2024) "Dual Sparse Structured Subspaces and Graph Regularisation for Particle Swarm Optimisation-Based Multi-Label Feature Selection". IEEE Computational Intelligence Magazine, 19(1), 36-50. doi:10.1109/MCI.2023.3327841
  • Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2024) "A genetic programming-based method for image classification with small training data". Knowledge-Based Systems, 283. doi:10.1016/j.knosys.2023.111188
  • Zhu, L., Zhang, F., Feng, M., Chen, K., Zhu, X., & Zhang, M. (2024) "Crossover Operators between Multiple Scheduling Heuristics with Genetic Programming for Dynamic Flexible Job Shop Scheduling". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10612212
  • Zhao, J., Xue, B., & Zhang, M. (2024) "SALENet: Structure-Aware Lighting Estimations from a Single Image for Indoor Environments". IEEE Transactions on Image Processing, 1. doi:10.1109/tip.2024.3512381
  • Zhao, J., Xue, B., Vennel, R., & Zhang, M. (2024) "Hierarchical mussel farm reconstruction from video with object tracking". Journal of the Royal Society of New Zealand. doi:10.1080/03036758.2024.2345316
  • Zhang, H., Zhou, A., Chen, Q., Xue, B., & Zhang, M. (2024) "SR-Forest: A Genetic Programming-Based Heterogeneous Ensemble Learning Method". IEEE Transactions on Evolutionary Computation, 28(5), 1484-1498. doi:10.1109/TEVC.2023.3243172
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2024) "P-Mixup: Improving Generalization Performance of Evolutionary Feature Construction with Pessimistic Vicinal Risk Minimization". In Unknown Book (Vol. 15148 LNCS, pp. 201-220). doi:10.1007/978-3-031-70055-2_13
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2024) "Modular Multitree Genetic Programming for Evolutionary Feature Construction for Regression". IEEE Transactions on Evolutionary Computation, 28(5), 1455-1469. doi:10.1109/TEVC.2023.3318638
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2024) "Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression". In Unknown Book (Vol. 14631 LNCS, pp. 142-158). doi:10.1007/978-3-031-56957-9_9
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2024) "Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction". In Unknown Book (Vol. 14326 LNAI, pp. 385-397). doi:10.1007/978-981-99-7022-3_36
  • Zhang, F., Shi, G., Mei, Y., & Zhang, M. (2024) "Multi-objective Dynamic Flexible Job Shop Scheduling with Biased Objectives via Multitask Genetic Programming". IEEE Transactions on Artificial Intelligence. doi:10.1109/TAI.2024.3456086
  • Yuan, G., Wang, B., Xue, B., & Zhang, M. (2024) "Particle Swarm Optimization for Efficiently Evolving Deep Convolutional Neural Networks Using an Autoencoder-Based Encoding Strategy". IEEE Transactions on Evolutionary Computation, 28(5), 1190-1204. doi:10.1109/TEVC.2023.3245322
  • Yu, K., Lian, J., Bi, Y., Liang, J., Xue, B., & Zhang, M. (2024) "An Automated and Interpretable Computer-Aided Approach for Skin Cancer Diagnosis Using Genetic Programming". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3459096
  • Yu, K., Lian, J., Bi, Y., Liang, J., Xue, B., & Zhang, M. (2024) "A genetic programming approach with adaptive region detection to skin cancer image classification". Journal of Automation and Intelligence. doi:10.1016/j.jai.2024.08.003
  • Yang, Y., Chen, G., Ma, H., Hartmann, S., & Zhang, M. (2024) "Dual-Tree Genetic Programming With Adaptive Mutation for Dynamic Workflow Scheduling in Cloud Computing". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3392968
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (2024) "Niching Genetic Programming to Learn Actions for Deep Reinforcement Learning in Dynamic Flexible Scheduling". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3395699
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (2024) "Genetic Programming with Lexicase Selection for Large-Scale Dynamic Flexible Job Shop Scheduling". IEEE Transactions on Evolutionary Computation, 28(5), 1235-1249. doi:10.1109/TEVC.2023.3244607
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (2024) "A Semantic Genetic Programming Approach to Evolving Heuristics for Multi-objective Dynamic Scheduling". In Unknown Book (Vol. 14472 LNAI, pp. 403-415). doi:10.1007/978-981-99-8391-9_32
  • Xu, J., Chen, Q., Xue, B., & Zhang, M. (2024) "A New Concordance Correlation Coefficient based Fitness Function for Genetic Programming for Symbolic Regression". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10611932
  • Xu, H., Xue, B., & Zhang, M. (2024) "Probe Population Based Initialization and Genetic Pool Based Reproduction for Evolutionary Bi-Objective Feature Selection". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3403655
  • Xu, H., Xue, B., & Zhang, M. (2024) "A Bi-Search Evolutionary Algorithm for High-Dimensional Bi-Objective Feature Selection". IEEE Transactions on Emerging Topics in Computational Intelligence, 8(5), 3489-3502. doi:10.1109/TETCI.2024.3393388
  • Xu, B., Xue, B., Schindler, J., & Zhang, M. (2024) "Ensemble learning based on neural networks for tree image segmentation". In 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024. doi:10.1109/MIGARS61408.2024.10544793
  • Wang, S., Mei, Y., & Zhang, M. (2024) "Explaining Genetic Programming-Evolved Routing Policies for Uncertain Capacitated Arc Routing Problems". IEEE Transactions on Evolutionary Computation, 28(4), 918-932. doi:10.1109/TEVC.2023.3238741
  • Wang, Q., Bi, Y., Xue, B., & Zhang, M. (2024) "Genetic Programming with Aggregate Channel Features for Flower Localization Using Limited Training Data". In Unknown Book (Vol. 14635 LNCS, pp. 196-211). doi:10.1007/978-3-031-56855-8_12
  • Wang, Q., Bi, Y., Xue, B., & Zhang, M. (2024) "Genetic Programming With Flexible Region Detection for Fine-Grained Image Classification". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3379257
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2024) "Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10612123
  • Wang, C., Chen, Q., Xue, B., & Zhang, M. (2024) "Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression". In Unknown Book (Vol. 1943 CCIS, pp. 163-176). doi:10.1007/978-981-99-8696-5_12
  • Viswambaran, R. A., Nekooei, M., Chen, G., & Xue, B. (2024) "Evolutionary Design of Long Short Term Memory Networks and Ensembles through Genetic Algorithms". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10612126
  • Viswambaran, R. A., Nekooei, M., Chen, G., & Xue, B. (2024) "Automatic Design of LSTM Networks with Skip Connections through Evolutionary and Differentiable Architecture Search". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10611997
  • Tian, Y., Mei, Y., & Zhang, M. (2024) "Learning Heuristics via Genetic Programming for Multi-Mode Resource-Constrained Project Scheduling". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10612172
  • Tang, S., Chen, Q., Xue, B., Huang, M., & Zhang, M. (2024) "Genetic Programming with Multi-Task Feature Selection for Alzheimer's Disease Diagnosis". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10611973
  • Sun, Z., Zhang, F., Mei, Y., Huang, H., Gu, C., Qian, B., & Zhang, M. (2024) "Evolving Scheduling Heuristics for Energy-Efficient Dynamic Workflow Scheduling in Cloud via Genetic Programming Hyper-Heuristics". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14862 LNCS (pp. 169-182). doi:10.1007/978-981-97-5578-3_14
  • Sun, Z., Xue, B., Zhang, M., & Schindler, J. (2024) "YOLOv8E: an efficient YOLOv8 method for instance segmentation of individual tree crowns in Wellington City, New Zealand". Journal of the Royal Society of New Zealand. doi:10.1080/03036758.2024.2424802
  • Sun, Z., Mei, Y., Zhang, F., Huang, H., Gu, C., & Zhang, M. (2024) "Multi-Tree Genetic Programming Hyper-Heuristic for Dynamic Flexible Workflow Scheduling in Multi-Clouds". IEEE Transactions on Services Computing, 17(5), 2687-2703. doi:10.1109/TSC.2024.3394691
  • Sun, Y., Pfahringer, B., Murilo Gomes, H., & Bifet, A. (2024) "Adaptive Prediction Interval for Data Stream Regression". In Unknown Book (Vol. 14647 LNAI, pp. 130-141). doi:10.1007/978-981-97-2259-4_10
  • Sun, F., Chen, G., Ma, H., & Hartmann, S. (2024) "Population-Based Incremental Learning for Effective IoT Service Composition with Replication". In Proceedings - 2024 IEEE International Conference on Software Services Engineering, SSE 2024 (pp. 74-76). doi:10.1109/SSE62657.2024.00022
  • Song, Z., Wang, H., Xue, B., Zhang, M., & Jin, Y. (2024) "Balancing Objective Optimization and Constraint Satisfaction in Expensive Constrained Evolutionary Multiobjective Optimization". IEEE Transactions on Evolutionary Computation, 28(5), 1286-1300. doi:10.1109/TEVC.2023.3300181
  • Saeed, A., Chen, G., Ma, H., & Fu, Q. (2024) "Genetic Algorithm with Repair Method for Deadline-Constrained IoT Workflow Scheduling in Fog-Cloud Computing". In IEEE International Conference on Cloud Computing, CLOUD (pp. 235-246). doi:10.1109/CLOUD62652.2024.00035
  • Rogers, M., Azhar, M., Schenone, S., Thrush, S., Xue, B., Zhang, M., & Delmas, P. (2024) "Evolving multispectral sensor configurations using genetic programming for estuary health monitoring". Journal of the Royal Society of New Zealand. doi:10.1080/03036758.2024.2393297
  • Rimas, M., Chen, Q., & Zhang, M. (2024) "Bloating Reduction in Symbolic Regression Through Function Frequency-Based Tree Substitution in Genetic Programming". In Unknown Book (Vol. 14472 LNAI, pp. 429-440). doi:10.1007/978-981-99-8391-9_34
  • Rimas, M., Anfar, M., Chen, Q., & Zhang, M. (2024) "Feature Selection for GPSR Based on Maximal Information Coefficient and Shapley Values". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10611755
  • Pei, W., Cui, Y., Xue, B., Zhang, M., Zhang, J., Hou, Y., . . . Qiang, Z. (2024) "DG-SMOTE: A Distance-Angle-Based Genetic Synthetic Minority Over-Sampling Technique for Unbalanced Data Learning". IEEE Transactions on Evolutionary Computation, 1. doi:10.1109/tevc.2024.3515485
  • Pang, J., Mei, Y., & Zhang, M. (2024) "Multi-Objective Genetic-Programming Hyper-Heuristic for Evolving Interpretable Flexible Job Shop Scheduling Rules". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10612158
  • Palli, A. S., JafreezalJaafar., Gilal, A. R., Alsughayyir, A., Gomes, H. M., Alshanqiti, A., & Omar, M. (2024) "Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review". Journal of Information and Communication Technology, 23(1), 105-139. doi:10.32890/jict2024.23.1.5
  • Nguyen, B. H., Xue, B., & Zhang, M. (2024) "Evolutionary Label Selection for Multi-label Classification". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10611918
  • Nguyen, B., Xue, B., Browne, W., & Zhang, M. (2024) "Evolutionary Classification". In Genetic and Evolutionary Computation (pp. 171-204). Springer Nature Singapore. doi:10.1007/978-981-99-3814-8_7
  • Mostofi, A., Jain, V., Mei, Y., & Benyoucef, L. (2024) "A new pricing mechanism for pharmaceutical supply chains: a game theory analytical approach for healthcare service". International Journal of Logistics Research and Applications, 27(7), 1228-1250. doi:10.1080/13675567.2022.2122421
  • Ming, F., Xue, B., Zhang, M., Gong, W., & Zhen, H. (2024) "Constrained Multi-objective Optimization via Relaxations on Both Constraints and Objectives". IEEE Transactions on Artificial Intelligence. doi:10.1109/TAI.2024.3454025
  • Lv, Z., Song, X., Feng, Y., Ou, Y., Sun, Y., & Zhang, M. (2024) "Evolutionary Neural Network Architecture Search". In Genetic and Evolutionary Computation (pp. 247-281). Springer Nature Singapore. doi:10.1007/978-981-99-3814-8_9
  • Londt, T., Gao, X., Andreae, P., & Mei, Y. (2024) "XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks". In Unknown Book (Vol. 14472 LNAI, pp. 416-428). doi:10.1007/978-981-99-8391-9_33
  • Liu, Z., Wang, R., Japkowicz, N., Gomes, H. M., Peng, B., & Zhang, W. (2024) "SeGDroid: An Android malware detection method based on sensitive function call graph learning[Formula presented]". Expert Systems with Applications, 235. doi:10.1016/j.eswa.2023.121125
  • Liu, Y., Zhang, F., Sun, Y., & Zhang, M. (2024) "Evolutionary Trainer-Based Deep Q-Network for Dynamic Flexible Job Shop Scheduling". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3367181
  • Liu, S., Xue, B., Song, M., Bao, H., & Zhang, M. (2024) "Multiobjective Optimization-Based Hyperspectral Unsupervised Band Selection for Anomaly Detection". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi:10.1109/JSTARS.2024.3508823
  • Liu, S., Song, M., Xue, B., Chang, C. I., & Zhang, M. (2024) "Hyperspectral Real-Time Local Anomaly Detection Based on Finite Markov via Line-by-Line Processing". IEEE Transactions on Geoscience and Remote Sensing, 62, 1-20. doi:10.1109/TGRS.2023.3345941
  • Liu, S., Cavalcanti Costa, J. G., Mei, Y., & Zhang, M. (2024) "GPGLS: Genetic Programming Guided Local Search for Large-Scale Vehicle Routing Problems". In Unknown Book (Vol. 15148 LNCS, pp. 36-51). doi:10.1007/978-3-031-70055-2_3
  • Lin, J., Chen, Q., Xue, B., & Zhang, M. (2024) "Evolutionary Sequential Transfer Learning for Multi-Objective Feature Selection in Classification". IEEE Transactions on Emerging Topics in Computational Intelligence, 1-15. doi:10.1109/tetci.2024.3451709
  • Liang, J., Yang, Z., Bi, Y., Qu, B., Liu, M., Xue, B., & Zhang, M. (2024) "A Multitree Genetic Programming-Based Feature Construction Approach to Crop Classification Using Hyperspectral Images". IEEE Transactions on Geoscience and Remote Sensing, 62. doi:10.1109/TGRS.2024.3415773
  • Koh, Y. S., Bifet, A., Bryan, K., Cassales, G., Graffeuille, O., Lim, N., . . . Gomes, H. (2024) "Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) Programme". In IJCAI International Joint Conference on Artificial Intelligence (pp. 7314-7322).
  • Johnson, D., Chen, G., & Lu, Y. (2024) "Multi-Agent Scheduler for the Continuous Dynamic Flexible Job Shop Scheduling Problem". In IEEE International Conference on Automation Science and Engineering (pp. 2924-2930). doi:10.1109/CASE59546.2024.10711538
  • John, T. C., Ain, Q. U., Al-Sahaf, H., & Zhang, M. (2024) "Evolving Feature Extraction Models for Melanoma Detection: A Co-operative Co-evolution Approach". In Unknown Book (Vol. 14634 LNCS, pp. 413-429). doi:10.1007/978-3-031-56852-7_26
  • Jiao, R., Xue, B., & Zhang, M. (2024) "Sparse Learning-Based Feature Selection in Classification: A Multi-Objective Perspective". IEEE Transactions on Emerging Topics in Computational Intelligence, 1-15. doi:10.1109/tetci.2024.3449850
  • Jiao, R., Xue, B., & Zhang, M. (2024) "Learning to Preselection: A Filter-Based Performance Predictor for Multiobjective Feature Selection in Classification". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3373802
  • Huang, Z., Mei, Y., Zhang, F., & Zhang, M. (2024) "Toward Evolving Dispatching Rules With Flow Control Operations By Grammar-Guided Linear Genetic Programming". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3353207
  • Huang, V., Wang, C., Datta, S., Chen, B., Chen, G., & Ma, H. (2024) "Evolving Epidemic Management Rules Using Deep Neuroevolution: A Novel Approach to Inspection Scheduling and Outbreak Minimization". In Unknown Book (Vol. 14472 LNAI, pp. 387-399). doi:10.1007/978-981-99-8391-9_31
  • Huang, J., Xue, B., Sun, Y., Zhang, M., & Yen, G. G. (2024) "Split-Level Evolutionary Neural Architecture Search With Elite Weight Inheritance". IEEE Transactions on Neural Networks and Learning Systems, 35(10), 13523-13537. doi:10.1109/TNNLS.2023.3269816
  • Heyden, M., Gomes, H. M., Fouché, E., Pfahringer, B., & Böhm, K. (2024) "Leveraging Plasticity in Incremental Decision Trees". In Lecture Notes in Computer Science (pp. 38-54). Springer Nature Switzerland. doi:10.1007/978-3-031-70362-1_3
  • Harper, M., Liu, I., Xue, B., Vennell, R., & Zhang, M. (2024) "Evaluating Machine Learning Techniques for Predicting Salinity". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10612099
  • Hancer, E., Xue, B., & Zhang, M. (2024) "Artificial Bee Colony for Multi-Label Feature Selection: A Logic-Guided Approach With Correlation-Driven Refinement". IEEE Transactions on Emerging Topics in Computational Intelligence. doi:10.1109/TETCI.2024.3502471
  • Hancer, E., Xue, B., & Zhang, M. (2024) "A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification". IEEE Transactions on Artificial Intelligence, 5(9), 4428-4442. doi:10.1109/TAI.2024.3380590
  • Han, Z., Wang, C., Fang, Z., Ma, H., & Chen, G. (2024) "Multi-Objective Optimization of Application Deployment Strategies in Integrated Cloud-Fog Computing Environments". In Proceedings - 2024 IEEE International Conference on Software Services Engineering, SSE 2024 (pp. 160-166). doi:10.1109/SSE62657.2024.00033
  • Guo, T., Mei, Y., Zhang, M., Sun, R., Zhu, Y., & Du, W. (2024) "Genetic Programming With Multi-Fidelity Surrogates for Large-Scale Dynamic Air Traffic Flow Management". IEEE Transactions on Evolutionary Computation, 1. doi:10.1109/tevc.2024.3512552
  • Gunasekara, N., Pfahringer, B., Gomes, H. M., Bifet, A., & Koh, Y. S. (2024) "Recurrent Concept Drifts on Data Streams". In IJCAI International Joint Conference on Artificial Intelligence (pp. 8029-8037).
  • Gu, H., Wang, H., Mei, Y., Zhang, M., & Jin, Y. (2024) "Surrogate-Assisted Neighborhood Search With Only a Few Weight Vectors for Expensive Large-Scale Multiobjective Binary Optimization". IEEE Transactions on Evolutionary Computation, 1. doi:10.1109/tevc.2024.3512795
  • Feng, J., Lin, M., Shang, L., & Gao, X. (2024) "Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification". In 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 1996-2005).
  • Fang, Z., Ma, H., Chen, G., & Hartmann, S. (2024) "A Group Genetic Algorithm for Energy-Efficient Resource Allocation in Container-Based Clouds with Heterogeneous Physical Machines". In Unknown Book (Vol. 14472 LNAI, pp. 453-465). doi:10.1007/978-981-99-8391-9_36
  • Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2024) "Multi-Tree Genetic Programming for Learning Color and Multi-Scale Features in Image Classification". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2024.3384021
  • Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2024) "A Multi-Tree Genetic Programming-Based Ensemble Approach to Image Classification With Limited Training Data". IEEE Computational Intelligence Magazine, 19(4), 47-62. doi:10.1109/MCI.2024.3446148
  • Demir, K., Nguyen, B. H., Rooney, J. S., Xue, B., Zhang, M., Lagutin, K., . . . Killeen, D. P. (2024) "Improving Inference of Biochemical Composition in Marine Biomass via Genetic Algorithm-Based Feature Selection on Raman Spectroscopic Data". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10612136
  • de Silva, A., Chen, G., Ma, H., & Nekooei, S. M. (2024) "Leiden Fitness-Based Genetic Algorithm with Niching for Community Detection in Large Social Networks". In Unknown Book (Vol. 14326 LNAI, pp. 423-435). doi:10.1007/978-981-99-7022-3_39
  • Chen, Y., Zhao, P., & Chen, Q. (2024) "Forecasting the commuting generation using metropolis-informed GCN and the topological commuter portrait". Transportation. doi:10.1007/s11116-024-10504-6
  • Chen, Q., Xue, B., Browne, W., & Zhang, M. (2024) "Evolutionary Regression and Modelling". In Genetic and Evolutionary Computation (pp. 121-149). Springer Nature Singapore. doi:10.1007/978-981-99-3814-8_5
  • Brown, P. T., Wilson, D., West, K., Escott, K. R., Basabas, K., Ritchie, B., . . . Keegan, T. T. (2024) "Māori Algorithmic Sovereignty: Idea, Principles, and Use". Data Science Journal, 23. doi:10.5334/dsj-2024-015
  • Bi, Y., Liang, J., Xue, B., & Zhang, M. (2024) "A Genetic Programming Approach with Building Block Evolving and Reusing to Image Classification". IEEE Transactions on Evolutionary Computation, 28(5), 1366-1380. doi:10.1109/TEVC.2023.3284712
  • Al-Sahaf, H., Mesejo, P., Bi, Y., & Zhang, M. (2024) "Evolutionary deep learning for computer vision and image processing". Applied Soft Computing, 151. doi:10.1016/j.asoc.2023.111159
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2024) "Multitree Genetic Programming with Feature-Based Transfer Learning for Symbolic Regression on Incomplete Data". IEEE Transactions on Cybernetics, 54(7), 4014-4027. doi:10.1109/TCYB.2023.3270319
  • Ain, Q. U., Xue, B., Al-Sahaf, H., & Zhang, M. (2024) "Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming". In Unknown Book (Vol. 1943 CCIS, pp. 254-269). doi:10.1007/978-981-99-8696-5_18
  • Ain, Q. U., Al-Sahaf, H., Xue, B., & Zhang, M. (2024) "Exploring Genetic Programming Models in Computer-Aided Diagnosis of Skin Cancer Images". In 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings. doi:10.1109/CEC60901.2024.10612105
  • Ain, Q. U., Al-Sahaf, H., Xue, B., & Zhang, M. (2024) "Automatically Evolving Interpretable Feature Vectors Using Genetic Programming for an Ensemble Classifier in Skin Cancer Detection". IEEE Computational Intelligence Magazine, 19(3), 26-41. doi:10.1109/MCI.2024.3401342
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., Li, J., & Narag, M. (2024) "An Ontology-based Three-Stage Approach to Medical Text classification with Feature Selection by Particle Swarm Optimisation". Procedia Computer Science, 246, 2479-2488. doi:10.1016/j.procs.2024.09.471
  • Londt, T., Gao, X., Andreae, P., & Mei, Y. (2023) "XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks". __. doi:10.48550/arxiv.2312.07760
  • Zhang, F., Mei, Y., Nguyen, S., Tan, K. C., & Zhang, M. (2023) "Task Relatedness-Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling". IEEE Transactions on Evolutionary Computation, 27(6), 1705-1719. doi:10.1109/TEVC.2022.3199783
  • Wang, S., Mei, Y., & Zhang, M. (2023) "A Multi-Objective Genetic Programming Algorithm With α Dominance and Archive for Uncertain Capacitated Arc Routing Problem". IEEE Transactions on Evolutionary Computation, 27(6), 1633-1647. doi:10.1109/TEVC.2022.3195165
  • Li, B., Guo, T., Mei, Y., Li, Y., Chen, J., Zhang, Y., . . . Du, W. (2023) "A multi-objective memetic algorithm with adaptive local search for airspace complexity mitigation". Swarm and Evolutionary Computation, 83. doi:10.1016/j.swevo.2023.101400
  • Jiao, R., Xue, B., & Zhang, M. (2023) "Benefiting From Single-Objective Feature Selection to Multiobjective Feature Selection: A Multiform Approach". IEEE Transactions on Cybernetics, 53(12), 7773-7786. doi:10.1109/TCYB.2022.3218345
  • Cai, X., Wang, K., Mei, Y., Li, Z., Zhao, J., & Zhang, Q. (2023) "Decomposition-Based Lin-Kernighan Heuristic With Neighborhood Structure Transfer for Multi/Many-Objective Traveling Salesman Problem". IEEE Transactions on Evolutionary Computation, 27(6), 1604-1617. doi:10.1109/TEVC.2022.3215174
  • Hancer, E., Xue, B., & Zhang, M. (2023) "An evolutionary filter approach to feature selection in classification for both single- and multi-objective scenarios". Knowledge-Based Systems, 280. doi:10.1016/j.knosys.2023.111008
  • Lythe, M., Mazorra de Cos, G., Mingallon, M., Lensen, A., Galloway, C., Knox, D., . . . Kumarasinghe, K. (2023) "Explainable AI – building trust through understanding: Explainable AI Whitepaper". AI Forum. Retrieved from https://aiforum.org.
  • Zhao, J., Xue, B., Vennell, R., & Zhang, M. (2023) "Large-Scale Mussel Farm Reconstruction with GPS Auxiliary". In International Conference on Image and Vision Computing New Zealand (IVCNZ).
  • Zhao, J., McMillan, C., Xue, B., Vennell, R., & Zhang, M. (2023) "Buoy Detection under Extreme Low-light Illumination for Intelligent Mussel Farming (Best Paper Award)". In International Conference on Image and Vision Computing New Zealand (IVCNZ).
  • McMillan, C., Zhao, J., Xue, B., Vennell, R., & Zhang, M. (2023) "Improving Buoy Detection with Deep Transfer Learning for Mussel Farm Automation". In International Conference on Image and Vision Computing New Zealand (IVCNZ 2023).
  • Zhang, Y., Mei, Y., Zhang, H., Cai, Q., & Wu, H. (2023) "RoCaSH2: An Effective Route Clustering and Search Heuristic for Large-Scale Multi-Depot Capacitated Arc Routing Problem". IEEE Computational Intelligence Magazine, 18(4), 43-56. doi:10.1109/MCI.2023.3304081
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2023) "MAP-Elites for Genetic Programming-Based Ensemble Learning: An Interactive Approach [AI-eXplained]". IEEE Computational Intelligence Magazine, 18(4), 62-63. doi:10.1109/MCI.2023.3304085
  • Shi, T., Ma, H., Chen, G., & Hartmann, S. (2023) "Auto-Scaling Containerized Applications in Geo-Distributed Clouds". IEEE Transactions on Services Computing, 16(6), 4261-4274. doi:10.1109/TSC.2023.3317262
  • Raymond, C., Chen, Q., Xue, B., & Zhang, M. (2023) "Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning". IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 13699-13714. doi:10.1109/TPAMI.2023.3294394
  • Cerqueira, V., Gomes, H. M., Bifet, A., & Torgo, L. (2023) "STUDD: a student–teacher method for unsupervised concept drift detection". Machine Learning, 112(11), 4351-4378. doi:10.1007/s10994-022-06188-7
  • Lee, A., Zhang, Y., Gomes, H. M., Bifet, A., & Pfahringer, B. (2023) "Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning". __. doi:10.48550/arxiv.2310.20052
  • Lee, A., Zhang, Y., Gomes, H. M., Bifet, A., & Pfahringer, B. (2023) "Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning". In International Conference on Information and Knowledge Management, Proceedings (pp. 4038-4042). doi:10.1145/3583780.3615236
  • Lensen, A. (2023) "NZ’s political leaders are ignoring the mounting threats from AI – and that’s putting everyone at risk". The Conversation. Retrieved from https://theconversation.
  • Zhang, F., Mei, Y., Nguyen, S., Tan, K. C., & Zhang, M. (2023) "Instance-Rotation-Based Surrogate in Genetic Programming with Brood Recombination for Dynamic Job-Shop Scheduling". IEEE Transactions on Evolutionary Computation, 27(5), 1192-1206. doi:10.1109/TEVC.2022.3180693
  • Yu, H., Fan, X., Hou, Y., Pei, W., Ge, H., Yang, X., . . . Zhang, M. (2023) "Toward Realistic 3D Human Motion Prediction With a Spatio-Temporal Cross- Transformer Approach". IEEE Transactions on Circuits and Systems for Video Technology, 33(10), 5707-5720. doi:10.1109/TCSVT.2023.3255186
  • Yang, C., Cheung, Y. M., Ding, J., Tan, K. C., Xue, B., & Zhang, M. (2023) "Contrastive Learning Assisted-Alignment for Partial Domain Adaptation". IEEE Transactions on Neural Networks and Learning Systems, 34(10), 7621-7634. doi:10.1109/TNNLS.2022.3145034
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2023) "Differential Evolution With Duplication Analysis for Feature Selection in Classification". IEEE Transactions on Cybernetics, 53(10), 6676-6689. doi:10.1109/TCYB.2022.3213236
  • Huang, J., Xue, B., Sun, Y., Zhang, M., & Yen, G. G. (2023) "Particle Swarm Optimization for Compact Neural Architecture Search for Image Classification". IEEE Transactions on Evolutionary Computation, 27(5), 1298-1312. doi:10.1109/TEVC.2022.3217290
  • Liu, G., Chen, G., & Huang, V. (2023) "Policy ensemble gradient for continuous control problems in deep reinforcement learning". Neurocomputing, 548. doi:10.1016/j.neucom.2023.126381
  • Li, A. D., Xue, B., & Zhang, M. (2023) "Multi-objective particle swarm optimization for key quality feature selection in complex manufacturing processes". Information Sciences, 641. doi:10.1016/j.ins.2023.119062
  • Cassales, G., Gomes, H. M., Bifet, A., Pfahringer, B., & Senger, H. (2023) "Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing". IEEE Transactions on Network and Service Management, 20(3), 3038-3054. doi:10.1109/TNSM.2022.3226505
  • McMillan, C., Zhao, J., Xue, B., Vennell, R., & Zhang, M. (2023) "Improving Buoy Detection with Deep Transfer Learning for Mussel Farm Automation". __. doi:10.48550/arxiv.2308.09238
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2023) "Feature clustering-Assisted feature selection with differential evolution". Pattern Recognition, 140. doi:10.1016/j.patcog.2023.109523
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2023) "Detecting Overlapping Areas in Unbalanced High-Dimensional Data Using Neighborhood Rough Set and Genetic Programming". IEEE Transactions on Evolutionary Computation, 27(4), 1130-1144. doi:10.1109/TEVC.2022.3203862
  • Nguyen, T. B., Browne, W. N., & Zhang, M. (2023) "ConCS: A Continual Classifier System for Continual Learning of Multiple Boolean Problems". IEEE Transactions on Evolutionary Computation, 27(4), 1057-1071. doi:10.1109/TEVC.2022.3210872
  • Li, S., Sun, Y., Yen, G. G., & Zhang, M. (2023) "Automatic Design of Convolutional Neural Network Architectures under Resource Constraints". IEEE Transactions on Neural Networks and Learning Systems, 34(8), 3832-3846. doi:10.1109/TNNLS.2021.3123105
  • Jiao, R., Xue, B., & Zhang, M. (2023) "A Multiform Optimization Framework for Constrained Multiobjective Optimization". IEEE Transactions on Cybernetics, 53(8), 5165-5177. doi:10.1109/TCYB.2022.3178132
  • Zhu, L., Zhang, F., Zhu, X., Chen, K., & Zhang, M. (2023) "Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling". In GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 384-392). doi:10.1145/3583131.3590440
  • Zhang, M., & Cagnoni, S. (2023) "Evolutionary Computation and Evolutionary Deep Learning for Image Analysis, Signal Processing and Pattern Recognition". In GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 1533-1561). doi:10.1145/3583133.3595045
  • Zhang, H., Zhou, A., Chen, Q., Xue, B., & Zhang, M. (2023) "Genetic Programming-based Evolutionary Feature Construction for Heterogeneous Ensemble Learning [Hot of the Press]". In GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 49-50). doi:10.1145/3583133.3595831
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2023) "A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression". In GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 1194-1202). doi:10.1145/3583131.3590365
  • Yuan, G., Xue, B., & Zhang, M. (2023) "An Effective One-Shot Neural Architecture Search Method with Supernet Fine-Tuning for Image Classification". In GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 615-623). doi:10.1145/3583131.3590438
  • Xue, B., & Zhang, M. (2023) "Evolutionary Computation for Feature Selection and Feature Construction". In GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 1129-1156). doi:10.1145/3583133.3595050
  • Ul Ain, Q., Al-Sahaf, H., Xue, B., & Zhang, M. (2023) "A New Genetic Programming Representation for Feature Learning in Skin Cancer Detection". In GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 707-710). doi:10.1145/3583133.3590550
  • Raymond, C., Chen, Q., Xue, B., & Zhang, M. (2023) "Fast and Efficient Local-Search for Genetic Programming Based Loss Function Learning". In GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 1184-1193). doi:10.1145/3583131.3590361
  • Liu, Y., Cui, Y., Xue, B., Browne, W. N., Cheng, W., Li, Y., & Zeng, L. (2023) "Absumption and Subsumption based Learning Classifier System for Real-World Continuous-based Problems". In GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 299-302). doi:10.1145/3583133.3590564
  • Huang, J., Xue, B., Sun, Y., & Zhang, M. (2023) "Multi-Objective Evolutionary Search of Compact Convolutional Neural Networks with Training-Free Estimation". In GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 655-658). doi:10.1145/3583133.3590535
  • Demir, K., Nguyen, B., Xue, B., & Zhang, M. (2023) "Co-operative Co-evolutionary Many-objective Embedded Multi-label Feature Selection with Decomposition-based PSO". In GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 438-446). doi:10.1145/3583131.3590373
  • Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2023) "Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear Scaling". In GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 420-428). doi:10.1145/3583131.3595918
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2023) "Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling". IEEE Transactions on Cybernetics, 53(7), 4473-4486. doi:10.1109/TCYB.2022.3196887
  • Xu, M., Mei, Y., Zhu, S., Zhang, B., Xiang, T., Zhang, F., & Zhang, M. (2023) "Genetic Programming for Dynamic Workflow Scheduling in Fog Computing". IEEE Transactions on Services Computing, 16(4), 2657-2671. doi:10.1109/TSC.2023.3249160
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2023) "Multiobjective Differential Evolution for Feature Selection in Classification". IEEE Transactions on Cybernetics, 53(7), 4579-4593. doi:10.1109/TCYB.2021.3128540
  • Wang, C., Ma, H., Chen, G., & Hartmann, S. (2023) "Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition". IEEE Transactions on Evolutionary Computation, 27(3), 490-504. doi:10.1109/TEVC.2022.3170899
  • Sadeghiram, S., Ma, H., & Chen, G. (2023) "Multi-objective distributed Web service composition—A link-dominance driven evolutionary approach". Future Generation Computer Systems, 143, 163-178. doi:10.1016/j.future.2023.01.001
  • John, T. C., Abbasi, M. S., Al-Sahaf, H., Welch, I., & Jang-Jaccard, J. (2023) "Evolving malice scoring models for ransomware detection: An automated approach by utilising genetic programming and cooperative coevolution". Computers and Security, 129. doi:10.1016/j.cose.2023.103215
  • Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2023) "Genetic Programming for Image Classification: A New Program Representation With Flexible Feature Reuse". IEEE Transactions on Evolutionary Computation, 27(3), 460-474. doi:10.1109/TEVC.2022.3169490
  • Alavizadeh, H., Jang-Jaccard, J., Enoch, S. Y., Al-Sahaf, H., Welch, I., Camtepe, S. A., & Kim, D. D. (2023) "A Survey on Cyber Situation-Awareness Systems: Framework, Techniques, and Insights". ACM Computing Surveys, 55(5). doi:10.1145/3530809
  • Chen, G., & Huang, V. (2023) "Deep Metric Tensor Regularized Policy Gradient". __. doi:10.48550/arXiv.2305.11017
  • Xiao, Q., Niu, B., Xue, B., & Hu, L. (2023) "Graph Convolutional Reinforcement Learning for Advanced Energy-Aware Process Planning". IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(5), 2802-2814. doi:10.1109/TSMC.2022.3219407
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2023) "Feature Selection Using Diversity-Based Multi-objective Binary Differential Evolution". Information Sciences, 626, 586-606. doi:10.1016/j.ins.2022.12.117
  • Jia, Y. H., Mei, Y., & Zhang, M. (2023) "Learning Heuristics with Different Representations for Stochastic Routing". IEEE Transactions on Cybernetics, 53(5), 3205-3219. doi:10.1109/TCYB.2022.3169210
  • Bi, Y., Xue, B., & Zhang, M. (2023) "Multitask Feature Learning as Multiobjective Optimization: A New Genetic Programming Approach to Image Classification". IEEE Transactions on Cybernetics, 53(5), 3007-3020. doi:10.1109/TCYB.2022.3174519
  • Ain, Q. U., Al-Sahaf, H., Xue, B., & Zhang, M. (2023) "Automatically Diagnosing Skin Cancers from Multimodality Images Using Two-Stage Genetic Programming". IEEE Transactions on Cybernetics, 53(5), 2727-2740. doi:10.1109/TCYB.2022.3182474
  • Gomes, H. M., Grzenda, M., Mello, R., Read, J., Le Nguyen, M. H., & Bifet, A. (2023) "A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams". ACM Computing Surveys, 55(4). doi:10.1145/3523055
  • Barddal, J. P., Gomes, H. M., & Enembreck, F. (2023) "Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory". __. doi:10.48550/arxiv.2304.09788
  • Zeng, P., Song, X., Lensen, A., Ou, Y., Sun, Y., Zhang, M., & Lv, J. (2023) "Differentiable Genetic Programming for High-dimensional Symbolic Regression". __. doi:10.48550/arXiv.2304.08915
  • Lensen, A. (2023) "Genetic Programming, Explainability, and Interdisciplinary AI (Invited Talk)". Centrum Wiskunde & Informatica (CWI).
  • Schofield, F., Slyfield, L., & Lensen, A. (2023) "A Genetic Programming Encoder for Increasing Autoencoder Interpretability". In 26th European Conference on Genetic Programming. Brno, Czech Republic.
  • Mei, Y. (2023) "Invited Speaker of "Genetic Programming and Reinforcement Learning for Dynamic Scheduling"". IEEE Computational Intelligence Society Early Career Researcher Forum.
  • Yazdani-Asrami, M., Song, W., Morandi, A., De Carne, G., Murta-Pina, J., Pronto, A., . . . Abraham, A. (2023) "Roadmap on artificial intelligence and big data techniques for superconductivity". Superconductor Science and Technology, 36(4). doi:10.1088/1361-6668/acbb34
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2023) "Differential Evolution-Based Feature Selection: A Niching-Based Multiobjective Approach". IEEE Transactions on Evolutionary Computation, 27(2), 296-310. doi:10.1109/TEVC.2022.3168052
  • Ma, J., Gao, X., & Li, Y. (2023) "Multi-generation multi-criteria feature construction using Genetic Programming". Swarm and Evolutionary Computation, 78. doi:10.1016/j.swevo.2023.101285
  • Ardeh, M. A., Mei, Y., Zhang, M., & Yao, X. (2023) "Knowledge Transfer Genetic Programming With Auxiliary Population for Solving Uncertain Capacitated Arc Routing Problem". IEEE Transactions on Evolutionary Computation, 27(2), 311-325. doi:10.1109/TEVC.2022.3169289
  • Bifet, A., Gama, J., Gomes, H. M., & Veloso, B. (2023) "EDITORIAL MESSAGE: Special Track on Data Streams". Proceedings of the ACM Symposium on Applied Computing, 381.
  • Jia, Y. H., Mei, Y., & Zhang, M. (2023) "A Two-Stage Swarm Optimizer with Local Search for Water Distribution Network Optimization". IEEE Transactions on Cybernetics, 53(3), 1667-1681. doi:10.1109/TCYB.2021.3107900
  • Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Tan, K. C. (2023) "A Survey on Evolutionary Neural Architecture Search". IEEE Transactions on Neural Networks and Learning Systems, 34(2), 550-570. doi:10.1109/TNNLS.2021.3100554
  • Ceschin, F., Botacin, M., Gomes, H. M., Pinagé, F., Oliveira, L. S., & Grégio, A. (2023) "Fast & Furious: On the modelling of malware detection as an evolving data stream[Formula presented]". Expert Systems with Applications, 212. doi:10.1016/j.eswa.2022.118590
  • Bi, Y., Xue, B., & Zhang, M. (2023) "Instance Selection-Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification". IEEE Transactions on Cybernetics, 53(2), 1118-1132. doi:10.1109/TCYB.2021.3105696
  • Bi, Y., Xue, B., Mesejo, P., Cagnoni, S., & Zhang, M. (2023) "A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends". IEEE Transactions on Evolutionary Computation, 27(1), 5-25. doi:10.1109/TEVC.2022.3220747
  • Raymond, C., Chen, Q., Xue, B., & Zhang, M. (2023) "Online Loss Function Learning". __. doi:10.48550/arxiv.2301.13247
  • O'Sullivan, F., Escott, K., Shaw, R., & Lensen, A. (2023) "Feature-based Image Matching for Identifying Individual Kākā". __. doi:10.48550/arXiv.2301.06678
  • O'Sullivan, F., Escott, K., Shaw, R., & Lensen, A. (2023) "Feature-based Image Matching for Identifying Individual Kākā". __. doi:10.48550/arXiv.2301.06678
  • Dang, B., Nguyen, A., Hong, Y., Nguyen, P., & Tran, B. -N. (2023) "Revealing the Hidden Structure of Affective States During Emotion Regulation in Synchronous Online Collaborative Learning". In The 56th Hawaii International Conference on System Sciences (HICSS).. Hawaii International Conference on System Sciences. Retrieved from https://hdl.handle.
  • Zhang, R., Sun, Y., & Zhang, M. (2023) "GPU Based Genetic Programming for Faster Feature Extraction in Binary Image Classification". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3294639
  • Zhang, H., Chen, Q., Xue, B., Banzhaf, W., & Zhang, M. (2023) "A Semantic-Based Hoist Mutation Operator for Evolutionary Feature Construction in Regression". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3331234
  • Zhang, H., Chen, Q., Tonda, A., Xue, B., Banzhaf, W., & Zhang, M. (2023) "MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning". In Unknown Book (Vol. 13986 LNCS, pp. 84-100). doi:10.1007/978-3-031-29573-7_6
  • Zeng, D., Liu, I., Bi, Y., Vennell, R., Briscoe, D., Xue, B., & Zhang, M. (2023) "A new multi-object tracking pipeline based on computer vision techniques for mussel farms". Journal of the Royal Society of New Zealand. doi:10.1080/03036758.2023.2240466
  • Zeng, D., Bi, Y., Liu, I., Xue, B., Vennell, R., & Zhang, M. (2023) "A New Genetic Programming-Based Approach to Object Detection in Mussel Farm Images". In International Conference Image and Vision Computing New Zealand. doi:10.1109/IVCNZ61134.2023.10343758
  • Yang, Y., Ma, H., Chen, G., & Hartmann, S. (2023) "A model-driven machine learning approach to dynamic multi-workflow scheduling". In CEUR Workshop Proceedings Vol. 3618.
  • Xue, B., Green, R., & Zhang, M. (2023) "Artificial Intelligence in New Zealand: applications and innovation". Journal of the Royal Society of New Zealand, 53(1), 1-5. doi:10.1080/03036758.2023.2170165
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (2023) "Genetic Programming for Dynamic Flexible Job Shop Scheduling: Evolution With Single Individuals and Ensembles". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3334626
  • Wang, C., Ma, H., Chen, G., Huang, V., Yu, Y., & Christopher, K. (2023) "Energy-Aware Dynamic Resource Allocation in Container-Based Clouds via Cooperative Coevolution Genetic Programming". In Unknown Book (Vol. 13989 LNCS, pp. 539-555). doi:10.1007/978-3-031-30229-9_35
  • Sun, Z., Xue, B., Zhang, M., & Schindler, J. (2023) "An Improved Mask R-CNN for Instance Segmentation of Tree Crowns in Aerial Imagery". In International Conference Image and Vision Computing New Zealand. doi:10.1109/IVCNZ61134.2023.10343827
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Preface". Studies in Computational Intelligence, 1070, v-vi. doi:10.1515/9783110796018-201
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Internet Protocol Based Architecture Design". In Studies in Computational Intelligence (Vol. 1070, pp. 181-192). doi:10.1007/978-3-031-16868-0_10
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Hybrid GA and PSO for Architecture Design". In Studies in Computational Intelligence (Vol. 1070, pp. 171-180). doi:10.1007/978-3-031-16868-0_9
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances". Springer International Publishing. doi:10.1007/978-3-031-16868-0
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Evolutionary Computation". In Studies in Computational Intelligence (Vol. 1070, pp. 3-7). doi:10.1007/978-3-031-16868-0_1
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "End-to-End Performance Predictors". In Studies in Computational Intelligence (Vol. 1070, pp. 237-255). doi:10.1007/978-3-031-16868-0_14
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Encoding Space Based on Directed Acyclic Graphs". In Studies in Computational Intelligence (Vol. 1070, pp. 223-236). doi:10.1007/978-3-031-16868-0_13
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Distribution Training Framework for Architecture Design". In Studies in Computational Intelligence (Vol. 1070, pp. 311-329). doi:10.1007/978-3-031-16868-0_17
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Differential Evolution for Architecture Design". In Studies in Computational Intelligence (Vol. 1070, pp. 193-202). doi:10.1007/978-3-031-16868-0_11
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Deep Neural Networks". In Studies in Computational Intelligence (Vol. 1070, pp. 9-30). doi:10.1007/978-3-031-16868-0_2
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Deep Neural Architecture Pruning". In Studies in Computational Intelligence (Vol. 1070, pp. 257-279). doi:10.1007/978-3-031-16868-0_15
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Deep Neural Architecture Compression". In Studies in Computational Intelligence (Vol. 1070, pp. 281-310). doi:10.1007/978-3-031-16868-0_16
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Architecture Design for Variational Auto-Encoders". In Studies in Computational Intelligence (Vol. 1070, pp. 79-105). doi:10.1007/978-3-031-16868-0_5
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Architecture Design for Stacked AEs and DBNs". In Studies in Computational Intelligence (Vol. 1070, pp. 39-59). doi:10.1007/978-3-031-16868-0_3
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Architecture Design for Skip-Connection Based CNNs". In Studies in Computational Intelligence (Vol. 1070, pp. 147-170). doi:10.1007/978-3-031-16868-0_8
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Architecture Design for RBs and DBs Based CNNs". In Studies in Computational Intelligence (Vol. 1070, pp. 127-145). doi:10.1007/978-3-031-16868-0_7
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Architecture Design for Plain CNNs". In Studies in Computational Intelligence (Vol. 1070, pp. 109-126). doi:10.1007/978-3-031-16868-0_6
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Architecture Design for Convolutional Auto-Encoders". In Studies in Computational Intelligence (Vol. 1070, pp. 61-77). doi:10.1007/978-3-031-16868-0_4
  • Sun, Y., Yen, G. G., & Zhang, M. (2023) "Architecture Design for Analyzing Hyperspectral Images". In Studies in Computational Intelligence (Vol. 1070, pp. 203-217). doi:10.1007/978-3-031-16868-0_12
  • Sun, F., Ma, H., Chen, G., & Hartmann, S. (2023) "IoT Service Composition - An Estimation of Distribution Algorithm with Adaptive Bias". In 2023 IEEE Congress on Evolutionary Computation, CEC 2023. doi:10.1109/CEC53210.2023.10254066
  • Song, Z., Wang, H., Xue, B., & Zhang, M. (2023) "Balancing Different Optimization Difficulty Between Objectives in Multi-Objective Feature Selection". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3334233
  • Shi, T., Hartmann, S., Chen, G., & Ma, H. (2023) "Location-Aware Cloud Service Brokering in Multi-cloud Environment". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13821 LNCS (pp. 408-410). doi:10.1007/978-3-031-26507-5_42
  • Saeed, A., Chen, G., Ma, H., & Fu, Q. (2023) "A Memetic Genetic Algorithm for Optimal IoT Workflow Scheduling". In Unknown Book (Vol. 13989 LNCS, pp. 556-572). doi:10.1007/978-3-031-30229-9_36
  • Rogers, M., Debski, I., Fischer, J., McComb, P., Frost, P., Xue, B., . . . Delmas, P. (2023) "Genetic Programming with Convolutional Operators for Albatross Nest Detection from Satellite Imaging". In Lecture Notes in Computer Science (pp. 287-298). Springer Nature Switzerland. doi:10.1007/978-3-031-45382-3_24
  • Palli, A. S., Jaafar, J., Hashmani, M. A., Gomes, H. M., Alsughayyir, A., & Gilal, A. R. (2023) "Combined Effect of Concept Drift and Class Imbalance on Model Performance during Stream Classification". Computers, Materials and Continua, 75(1), 1827-1845. doi:10.32604/cmc.2023.033934
  • O'keeffe, H., Xue, B., Zhang, M., Hawes, N., & Lovell-Smith, C. (2023) "Real-Time Instance Segmentation Techniques using Neural Networks for the Assessment of Green-Lipped Mussels". In International Conference Image and Vision Computing New Zealand. doi:10.1109/IVCNZ61134.2023.10343726
  • Nguyen, B. H., Xue, B., Andreae, P., & Zhang, M. (2023) "Evolutionary Instance Selection With Multiple Partial Adaptive Classifiers for Domain Adaptation". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3346406
  • Masood, A., Chen, G., Mei, Y., Al-Sahaf, H., & Zhang, M. (2023) "Genetic Programming with Adaptive Reference Points for Pareto Local Search in Many-Objective Job Shop Scheduling". In AI 2023: Advances in Artificial Intelligence (pp. 466-478). Springer, Singapore. doi:10.1007/978-981-99-8391-9_37
  • Londt, T., Gao, X., Andreae, P., & Mei, Y. (2023) "A Two-Stage Hybrid GA-Cellular Encoding Approach to Neural Architecture Search". In 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 (pp. 1814-1820). doi:10.1109/SSCI52147.2023.10371929
  • Lin, M., Shang, L., & Gao, X. (2023) "Enhancing Interpretability in AI-Generated Image Detection with Genetic Programming". In IEEE International Conference on Data Mining Workshops, ICDMW (pp. 371-378). doi:10.1109/ICDMW60847.2023.00053
  • Lin, J., Chen, Q., Xue, B., & Zhang, M. (2023) "Evolutionary Multitasking for Multi-Objective Feature Selection in Classification". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3338740
  • Lin, J., Chen, Q., Xue, B., & Zhang, M. (2023) "AMTEA-Based Multi-task Optimisation for Multi-objective Feature Selection in Classification". In Unknown Book (Vol. 13989 LNCS, pp. 623-639). doi:10.1007/978-3-031-30229-9_40
  • Knox, D., Xue, B., Zhang, M., & Cuff, J. (2023) "Measuring Ground Cover in Long Term Hill Country Photography using Weakly Supervised Convolutional Neural Networks". In International Conference Image and Vision Computing New Zealand. doi:10.1109/IVCNZ61134.2023.10343908
  • Knox, D., Xue, B., & Zhang, M. (2023) "Convolutional Neural Networks with Mask Shift Loss for Improving Building Outlines Detection". Procedia Computer Science, 222, 636-645. doi:10.1016/j.procs.2023.08.201
  • Jiao, R., Nguyen, B. H., Xue, B., & Zhang, M. (2023) "A Survey on Evolutionary Multiobjective Feature Selection in Classification: Approaches, Applications, and Challenges". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3292527
  • Huang, Z., Mei, Y., Zhang, F., & Zhang, M. (2023) "Multitask Linear Genetic Programming with Shared Individuals and its Application to Dynamic Job Shop Scheduling". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3263871
  • Guo, T., Mei, Y., Tang, K., & Du, W. (2023) "Cooperative Co-Evolution for Large-Scale Multi-Objective Air Traffic Flow Management". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3328886
  • Guo, T., Mei, Y., Tang, K., & Du, W. (2023) "A Knee-Guided Evolutionary Algorithm for Multi-Objective Air Traffic Flow Management". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2023.3281810
  • Gunasekara, N., Pfahringer, B., Gomes, H. M., & Bifet, A. (2023) "Survey on Online Streaming Continual Learning". In IJCAI International Joint Conference on Artificial Intelligence Vol. 2023-August (pp. 6628-6637).
  • Fu, W., Xue, B., Zhang, M., & Schindler, J. (2023) "Evolving U-Nets Using Genetic Programming for Tree Crown Segmentation". In Unknown Book (Vol. 13836 LNCS, pp. 188-201). doi:10.1007/978-3-031-25825-1_14
  • Fang, Z., Ma, H., Chen, G., & Hartmann, S. (2023) "Energy-Efficient and Communication-Aware Resource Allocation in Container-Based Cloud with Group Genetic Algorithm". In Unknown Book (Vol. 14419 LNCS, pp. 212-226). doi:10.1007/978-3-031-48421-6_15
  • Escott, K. R., Ma, H., & Chen, G. (2023) "Cooperative Coevolutionary Genetic Programming Hyper-Heuristic for Budget Constrained Dynamic Multi-workflow Scheduling in Cloud Computing". In Unknown Book (Vol. 13987 LNCS, pp. 146-161). doi:10.1007/978-3-031-30035-6_10
  • Culpi Mann, E., Gomes, H. M., Williamson, A. J., & Castelo Branco, M. (2023) "A decade of biodiversity conservation: insights into corporate social responsibility in an emerging market context". International Journal of Emerging Markets. doi:10.1108/IJOEM-05-2022-0744
  • Chen, Y., Shi, T., Ma, H., & Chen, G. (2023) "Multi-objective Location-Aware Service Brokering in Multi-cloud - A GPHH Approach with Transfer Learning". In Unknown Book (Vol. 13989 LNCS, pp. 573-587). doi:10.1007/978-3-031-30229-9_37
  • Chen, G., & Huang, V. (2023) "Ensemble Reinforcement Learning in Continuous Spaces - A Hierarchical Multi-Step Approach for Policy Training". In IJCAI International Joint Conference on Artificial Intelligence Vol. 2023-August (pp. 3514-3522). doi:10.24963/ijcai.2023/391
  • Burmester, G., Kugelmann, D., Steinmetz, D., Ma, H., & Hartmann, S. (2023) "A Conceptual Modeling Approach for Risk Assessment and Mitigation in Collision-Free UAV Routing Planning for Beyond-the-Visual-Line-of-Sight Flights". In Unknown Book (Vol. 14320 LNCS, pp. 394-411). doi:10.1007/978-3-031-47262-6_21
  • Bi, Y., Xue, B., Briscoe, D., Vennell, R., & Zhang, M. (2023) "A new artificial intelligent approach to buoy detection for mussel farming". Journal of the Royal Society of New Zealand, 53(1), 27-51. doi:10.1080/03036758.2022.2090966
  • Azam, B., Verma, B., & Zhang, M. (2023) "Context-Adaptive Deep Learning for Efficient Image Parsing in Remote Sensing: An Automated Parameter Selection Approach". In 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 (pp. 959-964). doi:10.1109/SSCI52147.2023.10372046
  • Mei, Y. (2022) "Invited Speaker of “Explainable Genetic Programming and its Applications in Combinatorial Optimisation"". IEEE Guangzhou Chapter Emerging Technology Forum.
  • Wang, B., Xue, B., & Zhang, M. (2022) "An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks". __. doi:10.48550/arxiv.2212.03942
  • Stanley, M., Lensen, A., & Zhang, M. (2022) "Using Neural Networks to Automate Monitoring of Fish Stocks". __.
  • Xie, X., Liu, Y., Sun, Y., Yen, G. G., Xue, B., & Zhang, M. (2022) "BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search". IEEE Transactions on Evolutionary Computation, 26(6), 1473-1485. doi:10.1109/TEVC.2022.3147526
  • Jia, Y. H., Mei, Y., & Zhang, M. (2022) "Confidence-Based Ant Colony Optimization for Capacitated Electric Vehicle Routing Problem With Comparison of Different Encoding Schemes". IEEE Transactions on Evolutionary Computation, 26(6), 1394-1408. doi:10.1109/TEVC.2022.3144142
  • Gao, G., Mei, Y., Xin, B., Jia, Y. H., & Browne, W. N. (2022) "Automated Coordination Strategy Design Using Genetic Programming for Dynamic Multipoint Dynamic Aggregation". IEEE Transactions on Cybernetics, 52(12), 13521-13535. doi:10.1109/TCYB.2021.3080044
  • Cai, X., Sun, Q., Li, Z., Xiao, Y., Mei, Y., Zhang, Q., & Li, X. (2022) "Cooperative Coevolution With Knowledge-Based Dynamic Variable Decomposition for Bilevel Multiobjective Optimization". IEEE Transactions on Evolutionary Computation, 26(6), 1553-1565. doi:10.1109/TEVC.2022.3154057
  • Lensen, A., & Betkier, M. (2022) "We built an algorithm that predicts the length of court sentences – could AI play a role in the justice system?". The Conversation. Retrieved from https://theconversation.
  • Wang, B., Pei, W., Xue, B., & Zhang, M. (2022) "Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations". __. doi:10.48550/arxiv.2211.15143
  • Mei, Y. (2022) "AI for Emergency Medical Dispatch". New Zealand AI Researcher Association Workshop 2022.
  • Lensen, A. (2022) "AI & Image Analysis: Computational Pathology in Aotearoa Online Hui". __.
  • Nguyen, L., Nguyen Vo, T. H., Trinh, Q. H., Nguyen, B. H., Nguyen-Hoang, P. U., Le, L., & Nguyen, B. P. (2022) "IANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation". Journal of Chemical Information and Modeling, 62(21), 5080-5089. doi:10.1021/acs.jcim.1c00920
  • Palli, A. S., Jaafar, J., Gomes, H. M., Hashmani, M. A., & Gilal, A. R. (2022) "An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams". Applied Sciences (Switzerland), 12(22). doi:10.3390/app122211688
  • Rodger, H., Lensen, A., & Betkier, M. (2022) "Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand". Journal of the Royal Society of New Zealand, 53(1), 133-147. doi:10.1080/03036758.2022.2114506
  • Zhang, F., Mei, Y., Nguyen, S., Tan, K. C., & Zhang, M. (2022) "Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling". IEEE Transactions on Cybernetics, 52(10), 10515-10528. doi:10.1109/TCYB.2021.3065340
  • Peng, B., Bi, Y., Xue, B., Zhang, M., & Wan, S. (2022) "A Survey on Fault Diagnosis of Rolling Bearings". Algorithms, 15(10). doi:10.3390/a15100347
  • Jiao, R., Xue, B., & Zhang, M. (2022) "Investigating the Correlation Amongst the Objective and Constraints in Gaussian Process-Assisted Highly Constrained Expensive Optimization". IEEE Transactions on Evolutionary Computation, 26(5), 872-885. doi:10.1109/TEVC.2021.3120980
  • Jia, Y. H., Mei, Y., & Zhang, M. (2022) "A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem". IEEE Transactions on Cybernetics, 52(10), 10855-10868. doi:10.1109/TCYB.2021.3069942
  • Chen, K., Xue, B., Zhang, M., & Zhou, F. (2022) "Correlation-Guided Updating Strategy for Feature Selection in Classification with Surrogate-Assisted Particle Swarm Optimization". IEEE Transactions on Evolutionary Computation, 26(5), 1015-1029. doi:10.1109/TEVC.2021.3134804
  • Bi, Y., Xue, B., & Zhang, M. (2022) "Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification". __. doi:10.48550/arxiv.2209.13233
  • Raymond, C., Chen, Q., Xue, B., & Zhang, M. (2022) "Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning". __. doi:10.48550/arxiv.2209.08907
  • Bi, Y., Xue, B., Mesejo, P., Cagnoni, S., & Zhang, M. (2022) "A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends". __. doi:10.48550/arxiv.2209.06399
  • Trujillo, L., Hu, T., Lourenço, N., & Zhang, M. (2022) "Editorial Introduction". Genetic Programming and Evolvable Machines, 23(3), 305-307. doi:10.1007/s10710-022-09437-9
  • Sun, Y., Pfahringer, B., Gomes, H. M., & Bifet, A. (2022) "SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams". Data Mining and Knowledge Discovery, 36(5), 2006-2032. doi:10.1007/s10618-022-00858-9
  • Nguyen, S., Thiruvady, D., Zhang, M., & Alahakoon, D. (2022) "Automated Design of Multipass Heuristics for Resource-Constrained Job Scheduling with Self-Competitive Genetic Programming". IEEE Transactions on Cybernetics, 52(9), 8603-8616. doi:10.1109/TCYB.2021.3062799
  • Ali, M., Chen, Y., Cree, M. J., & Zhang, M. (2022) "In vivo computation with sensor fusion and search acceleration for smart tumor homing". Computers in Biology and Medicine, 148. doi:10.1016/j.compbiomed.2022.105887
  • Li, N., Ma, L., Yu, G., Xue, B., Zhang, M., & Jin, Y. (2022) "Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues". __. doi:10.48550/arxiv.2208.10658
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2022) "Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling". In International Conference on Parallel Problem Solving from Nature (PPSN). Springer. doi:10.1007/978-3-031-14721-0_4
  • Rodger, H., Lensen, A., & Betkier, M. (2022) "Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand". __. doi:10.48550/arxiv.2208.06981
  • Mei, Y. (2022) "Keynote Speaker of “Automatic Heuristic Learning for Complex Optimisation Problems with Genetic Programming”". International Workshop on Evolutionary Computation and Learning.
  • Zhang, Y., Mei, Y., Huang, S., Zheng, X., & Zhang, C. (2022) "A Route Clustering and Search Heuristic for Large-Scale Multidepot-Capacitated Arc Routing Problem". IEEE Transactions on Cybernetics, 52(8), 8286-8299. doi:10.1109/TCYB.2020.3043265
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2022) "Collaborative Multifidelity-Based Surrogate Models for Genetic Programming in Dynamic Flexible Job Shop Scheduling". IEEE Transactions on Cybernetics, 52(8), 8142-8156. doi:10.1109/TCYB.2021.3050141
  • Wang, B., Xue, B., & Zhang, M. (2022) "Surrogate-Assisted Particle Swarm Optimization for Evolving Variable-Length Transferable Blocks for Image Classification". IEEE Transactions on Neural Networks and Learning Systems, 33(8), 3727-3740. doi:10.1109/TNNLS.2021.3054400
  • Sun, Y., Yen, G. G., Xue, B., Zhang, M., & Lv, J. (2022) "ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures". IEEE Transactions on Artificial Intelligence, 3(4), 526-540. doi:10.1109/TAI.2021.3128502
  • Shi, T., Ma, H., Chen, G., & Hartmann, S. (2022) "Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment". IEEE Transactions on Parallel and Distributed Systems, 33(8), 1982-1995. doi:10.1109/TPDS.2021.3133884
  • Mann, E., Williamson, A. J., Gomes, H. M., Dillon, S., & Bifet, A. (2022) "Should a Social Enterprise Publicly “Toot its Virtuous Horn”? An Artificial Intelligence Approach". Academy of Management Proceedings, 2022(1). doi:10.5465/ambpp.2022.15344abstract
  • Lu, M., Bi, Y., Xue, B., Hu, Q., Zhang, M., Wei, Y., . . . Wu, W. (2022) "Genetic Programming for High-Level Feature Learning in Crop Classification". Remote Sensing, 14(16). doi:10.3390/rs14163982
  • Lensen, A., Xue, B., & Zhang, M. (2022) "Genetic Programming for Manifold Learning: Preserving Local Topology". IEEE Transactions on Evolutionary Computation, 26(4), 661-675. doi:10.1109/TEVC.2021.3106672
  • Gao, G., Mei, Y., Jia, Y. H., Browne, W. N., & Xin, B. (2022) "Adaptive Coordination Ant Colony Optimization for Multipoint Dynamic Aggregation". IEEE Transactions on Cybernetics, 52(8), 7362-7376. doi:10.1109/TCYB.2020.3042511
  • Bi, Y., Xue, B., & Zhang, M. (2022) "Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification". IEEE Transactions on Cybernetics, 52(8), 8272-8285. doi:10.1109/TCYB.2021.3049778
  • Barracchia, E. P., Pio, G., Bifet, A., Gomes, H. M., Pfahringer, B., & Ceci, M. (2022) "LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding". Information Sciences, 606, 702-721. doi:10.1016/j.ins.2022.05.079
  • Ardeh, M. A., Mei, Y., & Zhang, M. (2022) "Genetic Programming with Knowledge Transfer and Guided Search for Uncertain Capacitated Arc Routing Problem". IEEE Transactions on Evolutionary Computation, 26(4), 765-779. doi:10.1109/TEVC.2021.3129278
  • Zhu, J., Jang-Jaccard, J., Welch, I., Al-Sahaf, H., & Camtepe, S. (2022) "Malware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework". __. doi:10.48550/arxiv.2207.10242
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (2022) "Genetic programming with cluster selection for dynamic flexible job shop scheduling". In IEEE Congress on Evolutionary Computation (CEC). doi:10.1109/CEC55065.2022.9870431
  • Mei, Y., & Lensen, A. (2022) "Tutorial on “Towards Better AI Interpretability Through Genetic Programming”". IEEE Congress on Evolutionary Computation 2022.
  • Zhang, M., & Cagnoni, S. (2022) "Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 1602-1630). doi:10.1145/3520304.3533636
  • Zeng, P., Lensen, A., & Sun, Y. (2022) "Large scale image classification using GPU-based genetic programming". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 619-622). doi:10.1145/3520304.3528892
  • Xue, B., & Zhang, M. (2022) "Evolutionary computation for feature selection and feature construction". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 1200-1229). doi:10.1145/3520304.3533659
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (2022) "Genetic programming with diverse partner selection for dynamic flexible job shop scheduling". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 615-618). doi:10.1145/3520304.3528920
  • Liu, Y., Browne, W. N., & Xue, B. (2022) "A comparison of rule compaction algorithms for michigan style learning classifier systems". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 31-32). doi:10.1145/3520304.3534068
  • Lin, J., Chen, Q., Xue, B., & Zhang, M. (2022) "Multi-task optimisation for multi-objective feature selection in classification". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 264-267). doi:10.1145/3520304.3528903
  • John, T. C., Abbasi, M. S., Al-Sahaf, H., & Welch, I. (2022) "Automatically Evolving Malice Scoring Models through Utilisation of Genetic Programming: A Cooperative Coevolution Approach". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 562-565). doi:10.1145/3520304.3529063
  • Durasević, M., Jakobović, D., Mei, Y., Nguyen, S., & Zhang, M. (2022) "Introduction to automated design of scheduling heuristics with genetic programming". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 1506-1526). doi:10.1145/3520304.3533667
  • Demir, K., Nguyen, B. H., Xue, B., & Zhang, M. (2022) "Particle swarm optimisation for sparsity-based feature selection in multi-label classification". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 232-235). doi:10.1145/3520304.3529074
  • Andersen, H., Lensen, A., & Browne, W. N. (2022) "Improving the search of learning classifier systems through interpretable feature clustering". In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 1752-1756). doi:10.1145/3520304.3534027
  • Wang, S., Mei, Y., & Zhang, M. (2022) "Local ranking explanation for genetic programming evolved routing policies for uncertain capacitated Arc routing problems". In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 314-322). doi:10.1145/3512290.3528723
  • Huang, Z., Mei, Y., Zhang, F., & Zhang, M. (2022) "Graph-based Linear Genetic Programming: A Case Study of Dynamic Scheduling". In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 955-963). doi:10.1145/3512290.3528730
  • Gao, G., Xin, B., Mei, Y., Lu, S., & Ding, S. (2022) "A multi-objective evolutionary algorithm with new reproduction and decomposition mechanisms for the multi-point dynamic aggregation problem". In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 1182-1190). doi:10.1145/3512290.3528728
  • Costa, J. G. C., Mei, Y., & Zhang, M. (2022) "Guided local search with an adaptive neighbourhood size heuristic for large scale vehicle routing problems". In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 213-221). doi:10.1145/3512290.3528865
  • Xie, X., Liu, Y., Sun, Y., Zhang, M., & Tan, K. C. (2022) "Architecture Augmentation for Performance Predictor Based on Graph Isomorphism". __. doi:10.48550/arxiv.2207.00987
  • Ruigrok, M., Xue, B., Catanach, A., Zhang, M., Jesson, L., Davy, M., & Wellenreuther, M. (2022) "The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus". Genes, 13(7). doi:10.3390/genes13071129
  • Johnson, D., Chen, G., & Lu, Y. (2022) "Multi-Agent Reinforcement Learning for Real-Time Dynamic Production Scheduling in a Robot Assembly Cell". IEEE Robotics and Automation Letters, 7(3), 7684-7691. doi:10.1109/LRA.2022.3184795
  • Chen, K., Xue, B., Zhang, M., & Zhou, F. (2022) "An Evolutionary Multitasking-Based Feature Selection Method for High-Dimensional Classification". IEEE Transactions on Cybernetics, 52(7), 7172-7186. doi:10.1109/TCYB.2020.3042243
  • Ain, Q. U., Al-Sahaf, H., Xue, B., & Zhang, M. (2022) "Genetic programming for automatic skin cancer image classification". Expert Systems with Applications, 197. doi:10.1016/j.eswa.2022.116680
  • Zhu, J., Jang-Jaccard, J., Singh, A., Welch, I., AL-Sahaf, H., & Camtepe, S. (2022) "A few-shot meta-learning based siamese neural network using entropy features for ransomware classification". Computers and Security, 117. doi:10.1016/j.cose.2022.102691
  • Wang, C., Ma, H., Chen, G., & Hartmann, S. (2022) "Memetic EDA-Based Approaches to QoS-Aware Fully Automated Semantic Web Service Composition". IEEE Transactions on Evolutionary Computation, 26(3), 570-584. doi:10.1109/TEVC.2021.3127633
  • Liu, Y., Browne, W. N., & Xue, B. (2022) "Visualizations for rule-based machine learning". Natural Computing, 21(2), 243-264. doi:10.1007/s11047-020-09840-0
  • Jia, Y. H., Mei, Y., & Zhang, M. (2022) "Contribution-Based Cooperative Co-Evolution for Nonseparable Large-Scale Problems With Overlapping Subcomponents". IEEE Transactions on Cybernetics, 52(6), 4246-4259. doi:10.1109/TCYB.2020.3025577
  • Chen, K., Xue, B., Zhang, M., & Zhou, F. (2022) "Evolutionary Multitasking for Feature Selection in High-Dimensional Classification via Particle Swarm Optimization". IEEE Transactions on Evolutionary Computation, 26(3), 446-460. doi:10.1109/TEVC.2021.3100056
  • Bi, Y., Xue, B., & Zhang, M. (2022) "Dual-Tree Genetic Programming for Few-Shot Image Classification". IEEE Transactions on Evolutionary Computation, 26(3), 555-569. doi:10.1109/TEVC.2021.3100576
  • Ceschin, F., Botacin, M., Gomes, H. M., Pinagé, F., Oliveira, L. S., & Grégio, A. (2022) "Fast & Furious: Modelling Malware Detection as Evolving Data Streams". __. doi:10.48550/arxiv.2205.12311
  • Tian, Y., Xiong, T., Liu, Z., Mei, Y., & Wan, L. (2022) "Multi-Objective multi-skill resource-constrained project scheduling problem with skill switches: Model and evolutionary approaches". Computers and Industrial Engineering, 167. doi:10.1016/j.cie.2021.107897
  • Madukwe, K. J., Gao, X., & Xue, B. (2022) "Token replacement-based data augmentation methods for hate speech detection". World Wide Web, 25(3), 1129-1150. doi:10.1007/s11280-022-01025-2
  • Bi, Y., Xue, B., & Zhang, M. (2022) "Using a small number of training instances in genetic programming for face image classification". Information Sciences, 593, 488-504. doi:10.1016/j.ins.2022.01.055
  • Abbasi, M. S., Al-Sahaf, H., Mansoori, M., & Welch, I. (2022) "Behavior-based ransomware classification: A particle swarm optimization wrapper-based approach for feature selection". Applied Soft Computing, 121. doi:10.1016/j.asoc.2022.108744
  • Bifet, A., Ferreira, C., Gama, J., & Gomes, H. M. (2022) "EDITORIAL MESSAGE Special Track on Data Streams". In Proceedings of the ACM Symposium on Applied Computing (pp. 449). doi:10.1145/3535426
  • Mandal, R., Azam, B., Verma, B., & Zhang, M. (2022) "Deep Learning Model with GA based Feature Selection and Context Integration". __. doi:10.48550/arxiv.2204.06189
  • Hancer, E., Xue, B., & Zhang, M. (2022) "Fuzzy filter cost-sensitive feature selection with differential evolution". Knowledge-Based Systems, 241. doi:10.1016/j.knosys.2022.108259
  • Fang, W., Zhu, H., & Mei, Y. (2022) "Hybrid meta-heuristics for the unrelated parallel machine scheduling problem with setup times". Knowledge-Based Systems, 241. doi:10.1016/j.knosys.2022.108193
  • Gupta, A., Ong, Y. S., De Jong, K. A., & Zhang, M. (2022) "Guest Editorial Special Issue on Multitask Evolutionary Computation". IEEE Transactions on Evolutionary Computation, 26(2), 202-205. doi:10.1109/TEVC.2022.3156325
  • Chen, Q., Xue, B., & Zhang, M. (2022) "Rademacher Complexity for Enhancing the Generalization of Genetic Programming for Symbolic Regression". IEEE Transactions on Cybernetics, 52(4), 2382-2395. doi:10.1109/TCYB.2020.3004361
  • Chen, F., Wei, J., Xue, B., & Zhang, M. (2022) "Feature fusion and kernel selective in Inception-v4 network". Applied Soft Computing, 119. doi:10.1016/j.asoc.2022.108582
  • Bi, Y., Xue, B., & Zhang, M. (2022) "Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning". IEEE Transactions on Evolutionary Computation, 26(2), 218-232. doi:10.1109/TEVC.2021.3097043
  • Wen, C., Lu, M., Bi, Y., Zhang, S., Xue, B., Zhang, M., . . . Wu, W. (2022) "An Object-Based Genetic Programming Approach for Cropland Field Extraction". Remote Sensing, 14(5). doi:10.3390/rs14051275
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2022) "High-Dimensional Unbalanced Binary Classification by Genetic Programming with Multi-Criterion Fitness Evaluation and Selection". Evolutionary Computation, 30(1), 99-129. doi:10.1162/evco_a_00304
  • Manapragada, C., Gomes, H. M., Salehi, M., Bifet, A., & Webb, G. I. (2022) "An eager splitting strategy for online decision trees in ensembles". Data Mining and Knowledge Discovery, 36(2), 566-619. doi:10.1007/s10618-021-00816-x
  • Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2022) "Genetic programming for feature extraction and construction in image classification". Applied Soft Computing, 118. doi:10.1016/j.asoc.2022.108509
  • Wang, S., Mei, Y., Zhang, M., & Yao, X. (2022) "Genetic Programming With Niching for Uncertain Capacitated Arc Routing Problem". IEEE Transactions on Evolutionary Computation, 26(1), 73-87. doi:10.1109/TEVC.2021.3095261
  • Wang, C., Ma, H., Chen, G., Hartmann, S., & Branke, J. (2022) "Robustness Estimation and Optimisation for Semantic Web Service Composition With Stochastic Service Failures". IEEE Transactions on Emerging Topics in Computational Intelligence, 6(1), 77-92. doi:10.1109/TETCI.2020.3027870
  • Haseeb, J., Mansoori, M., Hirose, Y., Al-Sahaf, H., & Welch, I. (2022) "Autoencoder-based feature construction for IoT attacks clustering". Future Generation Computer Systems, 127, 487-502. doi:10.1016/j.future.2021.09.025
  • Cassales, G., Gomes, H., Bifet, A., Pfahringer, B., & Senger, H. (2022) "Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing". __. doi:10.48550/arxiv.2201.06205
  • Zhao, Y., Chen, G., Ma, H., Zuo, X., & Ai, G. (2022) "Dynamic Bus Holding Control Using Spatial-Temporal Data – A Deep Reinforcement Learning Approach". In Unknown Book (Vol. 13728 LNAI, pp. 661-674). doi:10.1007/978-3-031-22695-3_46
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2022) "Phenotype Based Surrogate-Assisted Multi-objective Genetic Programming with Brood Recombination for Dynamic Flexible Job Shop Scheduling". In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 1218-1225). doi:10.1109/SSCI51031.2022.10022169
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2022) "Learning Strategies on Scheduling Heuristics of Genetic Programming in Dynamic Flexible Job Shop Scheduling". In 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. doi:10.1109/CEC55065.2022.9870243
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2022) "Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling". In Unknown Book (Vol. 13399 LNCS, pp. 48-62). doi:10.1007/978-3-031-14721-0_4
  • Yu, Y., Shi, T., Ma, H., & Chen, G. (2022) "A Genetic Programming-Based Hyper-Heuristic Approach for Multi-Objective Dynamic Workflow Scheduling in Cloud Environment". In 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. doi:10.1109/CEC55065.2022.9870403
  • Yang, Y., Chen, G., Ma, H., & Zhang, M. (2022) "Dual-Tree Genetic Programming for Deadline-Constrained Dynamic Workflow Scheduling in Cloud". In Unknown Book (Vol. 13740 LNCS, pp. 433-448). doi:10.1007/978-3-031-20984-0_31
  • Xue, B., Zhao, H., & Yao, W. (2022) "Deep Transfer Learning for IoT Intrusion Detection". In Proceedings - 2022 3rd International Conference on Computing, Networks and Internet of Things, CNIOT 2022 (pp. 88-94). doi:10.1109/CNIOT55862.2022.00023
  • Xu, M., Zhang, F., Mei, Y., & Zhang, M. (2022) "Genetic Programming with Multi-case Fitness for Dynamic Flexible Job Shop Scheduling". In 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. doi:10.1109/CEC55065.2022.9870340
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (2022) "Genetic Programming with Cluster Selection for Dynamic Flexible Job Shop Scheduling". __.
  • Xu, B., Bi, Y., Xue, B., Schindler, J., Martin, B., & Zhang, M. (2022) "Automatically Designing U-Nets Using A Genetic Algorithm for Tree Image Segmentation". In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 626-633). doi:10.1109/SSCI51031.2022.10022182
  • Wood, J., Nguyen, B. H., Xue, B., Zhang, M., & Killeen, D. (2022) "Automated Fish Classification Using Unprocessed Fatty Acid Chromatographic Data: A Machine Learning Approach". In Unknown Book (Vol. 13728 LNAI, pp. 516-529). doi:10.1007/978-3-031-22695-3_36
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2022) "Niching-Assisted Genetic Programming for Finding Multiple High-Quality Classifiers". In Unknown Book (Vol. 13728 LNAI, pp. 279-293). doi:10.1007/978-3-031-22695-3_20
  • Wang, B., Pei, W., Xue, B., & Zhang, M. (2022) "A Multi-objective Genetic Algorithm to Evolving Local Interpretable Model-agnostic Explanations for Deep Neural Networks in Image Classification". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2022.3225591
  • Trueman, J., Ma, H., Chen, A., & Hartmann, S. (2022) "Multi-objective Social Network Detection - A Graph Database Supported NSGA-II Based Approach". In Unknown Book (Vol. 13459 LNCS, pp. 21-33). doi:10.1007/978-3-031-15512-3_2
  • Tan, B., Ma, H., Mei, Y., & Zhang, M. (2022) "A Cooperative Coevolution Genetic Programming Hyper-Heuristics Approach for On-Line Resource Allocation in Container-Based Clouds". IEEE Transactions on Cloud Computing, 10(3), 1500-1514. doi:10.1109/TCC.2020.3026338
  • Steinmetz, D., Merz, F., Burmester, G., Ma, H., & Hartmann, S. (2022) "A Modeling Rule for Improving the Performance of Graph Models". In Unknown Book (Vol. 13607 LNCS, pp. 336-346). doi:10.1007/978-3-031-17995-2_24
  • Shi, G., Zhang, F., & Mei, Y. (2022) "A Novel Fitness Function for Genetic Programming in Dynamic Flexible Job Shop Scheduling". In 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. doi:10.1109/CEC55065.2022.9870235
  • Sargisson, F., Gao, X., & Xue, B. (2022) "Learning CNN architecture for multi-view text classification using genetic algorithms". In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 1507-1514). doi:10.1109/SSCI51031.2022.10022150
  • Robinson, D., Chen, Q., Xue, B., Killeen, D., Gordon, K. C., & Zhang, M. (2022) "A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment". In Unknown Book (Vol. 13224 LNCS, pp. 283-298). doi:10.1007/978-3-031-02462-7_19
  • Raymond, C., Chen, Q., Xue, B., & Zhang, M. (2022) "Multi-objective Genetic Programming with the Adaptive Weighted Splines Representation for Symbolic Regression". In Unknown Book (Vol. 13223 LNCS, pp. 51-67). doi:10.1007/978-3-031-02056-8_4
  • Petri, I., Chirila, I., Gomes, H. M., Bifet, A., & Rana, O. F. (2022) "Resource-Aware Edge-Based Stream Analytics". IEEE Internet Computing, 26(4), 79-88. doi:10.1109/MIC.2022.3152478
  • Pei, J., Mei, Y., Liu, J., & Yao, X. (2022) "An Investigation of Adaptive Operator Selection in Solving Complex Vehicle Routing Problem". In Unknown Book (Vol. 13629 LNCS, pp. 562-573). doi:10.1007/978-3-031-20862-1_41
  • Park, L. A. F., Gomes, H. M., Doborjeh, M., Zhao, Y., Williams, G., & Simoff, S. (2022) "Preface". Communications in Computer and Information Science, 1741 CCIS, v-vi.
  • Panda, S., Mei, Y., & Zhang, M. (2022) "Simplifying Dispatching Rules in Genetic Programming for Dynamic Job Shop Scheduling". In Unknown Book (Vol. 13222 LNCS, pp. 95-110). doi:10.1007/978-3-031-04148-8_7
  • Palli, A. S., Jaafar, J., Hashmani, M. A., Gomes, H. M., & Gilal, A. R. (2022) "A Hybrid Sampling Approach for Imbalanced Binary and Multi-Class Data Using Clustering Analysis". IEEE Access, 10, 118639-118653. doi:10.1109/ACCESS.2022.3218463
  • Masood, A., Chen, G., Mei, Y., Al-Sahaf, H., & Zhang, M. (2022) "Genetic Programming Hyper-heuristic with Gaussian Process-based Reference Point Adaption for Many-Objective Job Shop Scheduling". In 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. doi:10.1109/CEC55065.2022.9870322
  • Mandal, R., Azam, B., Verma, B., & Zhang, M. (2022) "Genetic Algorithms for Optimising Context-based Neural Networks for Image Segmentation". In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 642-648). doi:10.1109/SSCI51031.2022.10022103
  • MacLachlan, J., Mei, Y., Zhang, F., & Zhang, M. (2022) "Genetic Programming for Vehicle Subset Selection in Ambulance Dispatching". In 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. doi:10.1109/CEC55065.2022.9870323
  • Lu, Y., Nguyen, B. H., & Xue, B. (2022) "Label Clustering for Particle Swarm Optimisation based Multi-Label Feature Selection". In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 1515-1522). doi:10.1109/SSCI51031.2022.10022306
  • Liu, G., Ma, J., Hu, T., & Gao, X. (2022) "A feature selection method with feature ranking using genetic programming". Connection Science, 34(1), 1146-1168. doi:10.1080/09540091.2022.2049702
  • Lin, B. C., Liu, X. F., & Mei, Y. (2022) "Efficient Extended Ant Colony Optimization for Capacitated Electric Vehicle Routing". In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 504-511). doi:10.1109/SSCI51031.2022.10022179
  • Lee, A., Gomes, H. M., & Zhang, Y. (2022) "Balancing the Stability-Plasticity Dilemma with Online Stability Tuning for Continual Learning". In Proceedings of the International Joint Conference on Neural Networks Vol. 2022-July. doi:10.1109/IJCNN55064.2022.9892055
  • Jiao, R., Xue, B., & Zhang, M. (2022) "Solving Multi-objective Feature Selection Problems in Classification via Problem Reformulation and Duplication Handling". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2022.3215745
  • Jiao, R., Xue, B., & Zhang, M. (2022) "Handling Different Preferences Between Objectives for Multi-objective Feature Selection in Classification". In Unknown Book (Vol. 13728 LNAI, pp. 237-251). doi:10.1007/978-3-031-22695-3_17
  • Huang, Z., Zhang, F., Mei, Y., & Zhang, M. (2022) "An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling". In Unknown Book (Vol. 13223 LNCS, pp. 162-178). doi:10.1007/978-3-031-02056-8_11
  • Huang, Z., Mei, Y., Zhang, F., & Zhang, M. (2022) "A Further Investigation to Improve Linear Genetic Programming in Dynamic Job Shop Scheduling". In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 (pp. 496-503). doi:10.1109/SSCI51031.2022.10022208
  • Huang, V., Wang, C., Ma, H., Chen, G., & Christopher, K. (2022) "Cost-Aware Dynamic Multi-Workflow Scheduling in Cloud Data Center Using Evolutionary Reinforcement Learning". In Unknown Book (Vol. 13740 LNCS, pp. 449-464). doi:10.1007/978-3-031-20984-0_32
  • Huang, J., Xue, B., Sun, Y., & Zhang, M. (2022) "EDE-NAS: An Eclectic Differential Evolution Approach to Single-Path Neural Architecture Search". In Unknown Book (Vol. 13728 LNAI, pp. 116-130). doi:10.1007/978-3-031-22695-3_9
  • Gunasekara, N., Gomes, H. M., Pfahringer, B., & Bifet, A. (2022) "Online Hyperparameter Optimization for Streaming Neural Networks". In Proceedings of the International Joint Conference on Neural Networks Vol. 2022-July. doi:10.1109/IJCNN55064.2022.9891953
  • Gunasekara, N., Gomes, H., Bifet, A., & Pfahringer, B. (2022) "Adaptive Online Domain Incremental Continual Learning". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13529 LNCS (pp. 491-502). doi:10.1007/978-3-031-15919-0_41
  • Gunasekara, N., Gomes, H., Bifet, A., & Pfahringer, B. (2022) "Adaptive Neural Networks for Online Domain Incremental Continual Learning". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13601 LNAI (pp. 89-103). doi:10.1007/978-3-031-18840-4_7
  • Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2022) "Evolving Effective Ensembles for Image Classification Using Multi-objective Multi-tree Genetic Programming". In Unknown Book (Vol. 13728 LNAI, pp. 294-307). doi:10.1007/978-3-031-22695-3_21
  • Escott, K. R., Ma, H., & Chen, G. (2022) "Transfer Learning Assisted GPHH for Dynamic Multi-Workflow Scheduling in Cloud Computing". In Unknown Book (Vol. 13151 LNAI, pp. 440-451). doi:10.1007/978-3-030-97546-3_36
  • de Silva, A., Chen, A., Ma, H., & Nekooei, M. (2022) "Genetic Algorithm with a Novel Leiden-based Mutation Operator for Community Detection". In Unknown Book (Vol. 13728 LNAI, pp. 252-265). doi:10.1007/978-3-031-22695-3_18
  • Dang, B. T., Nguyen, B. H., & Andreae, P. (2022) "Operation-based Greedy Algorithm for Discounted Knapsack Problem". In Unknown Book (Vol. 13728 LNAI, pp. 646-660). doi:10.1007/978-3-031-22695-3_45
  • Curran, B., Nekooei, S. M., & Chen, G. (2022) "Accurate New Zealand Wildlife Image Classification-Deep Learning Approach". In Unknown Book (Vol. 13151 LNAI, pp. 632-644). doi:10.1007/978-3-030-97546-3_51
  • Chen, Y., Shi, T., Ma, H., & Chen, G. (2022) "Automatically Design Heuristics for Multi-Objective Location-Aware Service Brokering in Multi-Cloud". In Proceedings - 2022 IEEE International Conference on Services Computing, SCC 2022 (pp. 206-214). doi:10.1109/SCC55611.2022.00039
  • Chen, Q., Xue, B., & Zhang, M. (2022) "Genetic Programming for Instance Transfer Learning in Symbolic Regression". IEEE Transactions on Cybernetics, 52(1), 25-38. doi:10.1109/TCYB.2020.2969689
  • Chen, Q., & Xue, B. (2022) "Generalisation in Genetic Programming for Symbolic Regression: Challenges and Future Directions". In Women in Engineering and Science (pp. 281-302). Springer International Publishing. doi:10.1007/978-3-030-79092-9_13
  • Chanajitt, R., Pfahringer, B., Gomes, H. M., & Yogarajan, V. (2022) "Multiclass Malware Classification Using Either Static Opcodes or Dynamic API Calls". In Unknown Book (Vol. 13728 LNAI, pp. 427-441). doi:10.1007/978-3-031-22695-3_30
  • Chanajitt, R., Pfahringer, B., & Gomes, H. M. (2022) "A Comparison of Neural Network Architectures for Malware Classification Based on Noriben Operation Sequences". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13529 LNCS (pp. 428-440). doi:10.1007/978-3-031-15919-0_36
  • Andersen, H., Lensen, A., Browne, W. N., & Mei, Y. (2022) "Evolving Counterfactual Explanations with Particle Swarm Optimization and Differential Evolution". In 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. doi:10.1109/CEC55065.2022.9870283
  • Al-Sahaf, H., Gregory, M. A., & Welch, I. (2022) "Welcome Message". 2022 32nd International Telecommunication Networks and Applications Conference, ITNAC 2022. doi:10.1109/ITNAC55475.2022.9998403
  • Allen, J. M. U., Bennett, J. L., Clark, Z. L., Escott, K. R., Fa’avae, D. T. M., Kaulamatoa, J. L., . . . Woolner, V. H. (2022) "Relational and collective excellence: unfolding the potential of Pacific early career researchers". Journal of the Royal Society of New Zealand, 52(S1), 75-91. doi:10.1080/03036758.2022.2093228
  • Al Mamun, A., Al-Sahaf, H., Welch, I., & Camtepe, S. (2022) "Advanced Persistent Threat Detection: A Particle Swarm Optimization Approach". In 2022 32nd International Telecommunication Networks and Applications Conference, ITNAC 2022 (pp. 42-49). doi:10.1109/ITNAC55475.2022.9998358
  • Akindele, T., Tan, B., Mei, Y., & Ma, H. (2022) "Hybrid Grouping Genetic Algorithm for Large-Scale Two-Level Resource Allocation of Containers in the Cloud". In Unknown Book (Vol. 13151 LNAI, pp. 519-530). doi:10.1007/978-3-030-97546-3_42
  • Ain, Q. U. I., Xue, B., Al-Sahaf, H., & Zhang, M. (2022) "A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification". In IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2022-November (pp. 378-387). doi:10.1109/ICDMW58026.2022.00057
  • Moghaddam, S. A. V., Al-Sahaf, H., Xue, B., Hollitt, C., & Zhang, M. (2021) "An automatic feature construction method for salient object detection: A genetic programming approach[Formula presented]". Expert Systems with Applications, 186. doi:10.1016/j.eswa.2021.115726
  • Cassales, G., Gomes, H., Bifet, A., Pfahringer, B., & Senger, H. (2021) "Improving the performance of bagging ensembles for data streams through mini-batching". __. doi:10.48550/arxiv.2112.09834
  • Ha, T., & Gao, X. (2021) "Evolving Multi-view Autoencoders for Text Classification". In ACM International Conference Proceeding Series (pp. 270-276). doi:10.1145/3486622.3493969
  • O'Neill, D., Xue, B., & Zhang, M. (2021) "Evolutionary Neural Architecture Search for High-Dimensional Skip-Connection Structures on DenseNet Style Networks". IEEE Transactions on Evolutionary Computation, 25(6), 1118-1132. doi:10.1109/TEVC.2021.3083315
  • Nguyen, B. H., Xue, B., Andreae, P., & Zhang, M. (2021) "A Hybrid Evolutionary Computation Approach to Inducing Transfer Classifiers for Domain Adaptation". IEEE Transactions on Cybernetics, 51(12), 6319-6332. doi:10.1109/TCYB.2020.2980815
  • Bi, Y., Xue, B., & Zhang, M. (2021) "A Divide-And-Conquer Genetic Programming Algorithm with Ensembles for Image Classification". IEEE Transactions on Evolutionary Computation, 25(6), 1148-1162. doi:10.1109/TEVC.2021.3082112
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2021) "Multitree Genetic Programming with New Operators for Transfer Learning in Symbolic Regression with Incomplete Data". IEEE Transactions on Evolutionary Computation, 25(6), 1049-1063. doi:10.1109/TEVC.2021.3079843
  • Lensen, A. (2021) "Genetic Programming, Explainability, and Interdisciplinary AI". University of Canterbury.
  • Lensen, A., Xue, B., & Zhang, M. (2021) "Genetic programming for evolving a front of interpretable models for data visualization". IEEE Transactions on Cybernetics, 51(11), 5468-5482. doi:10.1109/TCYB.2020.2970198
  • Jiao, R., Zeng, S., Li, C., & Ong, Y. -S. (2021) "Two-type weight adjustments in MOEA/D for highly constrained many-objective optimization". Information Sciences, 578, 592-614. doi:10.1016/j.ins.2021.07.048
  • Cassales, G., Gomes, H., Bifet, A., Pfahringer, B., & Senger, H. (2021) "Improving the performance of bagging ensembles for data streams through mini-batching". Information Sciences, 580, 260-282. doi:10.1016/j.ins.2021.08.085
  • Alavizadeh, H., Jang-Jaccard, J., Enoch, S. Y., Al-Sahaf, H., Welch, I., Camtepe, S. A., & Kim, D. S. (2021) "A Survey on Threat Situation Awareness Systems: Framework, Techniques, and Insights". __. doi:10.48550/arxiv.2110.15747
  • Peng, B., Wan, S., Bi, Y., Xue, B., & Zhang, M. (2021) "Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis". IEEE Transactions on Cybernetics, 51(10), 4909-4923. doi:10.1109/TCYB.2020.3032945
  • Jiao, R., Zeng, S., Li, C., Yang, S., & Ong, Y. S. (2021) "Handling Constrained Many-Objective Optimization Problems via Problem Transformation". IEEE Transactions on Cybernetics, 51(10), 4834-4847. doi:10.1109/TCYB.2020.3031642
  • He, Y., Liu, X. H., Zhang, H. L., Zheng, W., Zhao, F. Y., Aurel Schnabel, M., & Mei, Y. (2021) "Hybrid framework for rapid evaluation of wind environment around buildings through parametric design, CFD simulation, image processing and machine learning". Sustainable Cities and Society, 73. doi:10.1016/j.scs.2021.103092
  • Ding, W., Pedrycz, W., Yen, G. G., & Xue, B. (2021) "Guest Editorial Evolutionary Computation Meets Deep Learning". IEEE Transactions on Evolutionary Computation, 25(5), 810-814. doi:10.1109/TEVC.2021.3096336
  • Chen, X., Sun, Y., Zhang, M., & Peng, D. (2021) "Evolving Deep Convolutional Variational Autoencoders for Image Classification". IEEE Transactions on Evolutionary Computation, 25(5), 815-829. doi:10.1109/TEVC.2020.3047220
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., Li, J., & Narag, M. (2021) "Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques". Artificial Intelligence in Medicine, 120. doi:10.1016/j.artmed.2021.102167
  • Lensen, A., Xue, B., & Zhang, M. (2021) "Genetic Programming for Manifold Learning: Preserving Local Topology". __. doi:10.48550/arxiv.2108.09914
  • Liu, Y., Browne, W. N., & Xue, B. (2021) "A Comparison of Learning Classifier Systems' Rule Compaction Algorithms for Knowledge Visualization". ACM Transactions on Evolutionary Learning and Optimization, 1(3). doi:10.1145/3468166
  • Xie, X., Liu, Y., Sun, Y., Yen, G. G., Xue, B., & Zhang, M. (2021) "BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search". __. doi:10.48550/arxiv.2108.03856
  • Zhang, F., Mei, Y., Nguyen, S., Zhang, M., & Tan, K. C. (2021) "Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling". IEEE Transactions on Evolutionary Computation, 25(4), 651-665. doi:10.1109/TEVC.2021.3065707
  • Sun, Y., Zhang, M., & Yen, G. G. (2021) "Evolutionary Neural Architecture Search and Applications [Guest Editorial]". IEEE Computational Intelligence Magazine, 16(3), 8-9. doi:10.1109/MCI.2021.3084391
  • Peng, B., Bi, Y., Xue, B., Zhang, M., & Wan, S. (2021) "Multi-View Feature Construction Using Genetic Programming for Rolling Bearing Fault Diagnosis [Application Notes]". IEEE Computational Intelligence Magazine, 16(3), 79-94. doi:10.1109/MCI.2021.3084495
  • Ai, Q., Zuo, X., Chen, G., & Wu, B. (2021) "Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable". Applied Soft Computing, Elsevier.
  • Ai, G., Zuo, X., chen, G., & Wu, B. (2021) "Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable". __. doi:10.48550/arxiv.2107.07066
  • Zhang, M., & Cagnoni, S. (2021) "Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition". In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 1169-1198). doi:10.1145/3449726.3461412
  • Xue, B., & Zhang, M. (2021) "Evolutionary computation for feature selection and feature construction". In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 1141-1168). doi:10.1145/3449726.3461415
  • Wang, B., Xue, B., & Zhang, M. (2021) "A transfer learning based evolutionary deep learning framework to evolve convolutional neural networks". In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 287-288). doi:10.1145/3449726.3459455
  • Wang, B., Pei, W., Xue, B., & Zhang, M. (2021) "Evolving local interpretable model-agnostic explanations for deep neural networks in image classification". In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 173-174). doi:10.1145/3449726.3459452
  • Raymond, C., Chen, Q., Xue, B., & Zhang, M. (2021) "Multi-objective genetic programming for symbolic regression with the adaptive weighted splines representation". In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 165-166). doi:10.1145/3449726.3459461
  • Fan, Q., Bi, Y., Xue, B., & Zhang, M. (2021) "Genetic programming with a new representation and a new mutation operator for image classification". In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 249-250). doi:10.1145/3449726.3459468
  • Demir, K., Nguyen, B. H., Xue, B., & Zhang, M. (2021) "Sparsity-based evolutionary multi-objective feature selection for multi-label classification". In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (pp. 147-148). doi:10.1145/3449726.3459467
  • Li, A. D., Xue, B., & Zhang, M. (2021) "Improved binary particle swarm optimization for feature selection with new initialization and search space reduction strategies". Applied Soft Computing, 106. doi:10.1016/j.asoc.2021.107302
  • Gomes, H. M., Read, J., Bifet, A., & Durrant, R. J. (2021) "Learning from evolving data streams through ensembles of random patches". Knowledge and Information Systems, 63(7), 1597-1625. doi:10.1007/s10115-021-01579-z
  • Wang, S., Mei, Y., & Zhang, M. (2021) "Two-stage multi-objective genetic programming with archive for uncertain capacitated arc routing problem". In GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference (pp. 287-295). doi:10.1145/3449639.3459298
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2021) "Genetic programming for borderline instance detection in high-dimensional unbalanced classification". In GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference (pp. 349-357). doi:10.1145/3449639.3459284
  • Ardeh, M. A., Mei, Y., & Zhang, M. (2021) "A novel multi-task genetic programming approach to uncertain capacitated Arc routing problem". In GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference (pp. 759-767). doi:10.1145/3449639.3459322
  • Gomes, H. M., Grzenda, M., Mello, R., Read, J., Nguyen, M. H. L., & Bifet, A. (2021) "A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams". __. doi:10.48550/arxiv.2106.09170
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2021) "Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling". IEEE Transactions on Evolutionary Computation, 25(3), 552-566. doi:10.1109/TEVC.2021.3056143
  • Nguyen, S., Thiruvady, D., Zhang, M., & Tan, K. C. (2021) "A Genetic Programming Approach for Evolving Variable Selectors in Constraint Programming". IEEE Transactions on Evolutionary Computation, 25(3), 492-507. doi:10.1109/TEVC.2021.3050465
  • Chen, Q., Xue, B., & Zhang, M. (2021) "Preserving Population Diversity Based on Transformed Semantics in Genetic Programming for Symbolic Regression". IEEE Transactions on Evolutionary Computation, 25(3), 433-447. doi:10.1109/TEVC.2020.3046569
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., & Li, J. (2021) "Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes". __. doi:10.26686/wgtn.14676090
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., & Li, J. (2021) "An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation". In Victoria University of Wellington Library. doi:10.26686/wgtn.14676087
  • Chen, G. (2021) "Learning Symbolic Expressions via Gumbel-Max Equation Learner Networks". arXiv.
  • Lensen, A. (2021) "Unsupervised Learning and Explainable AI (Joint VUW-MetService Seminar)". __.
  • Lensen, A. (2021) "AI in Facility Management". In BILD463: Built Facility Management.
  • Fu, W., Xue, B., Gao, X., & Zhang, M. (2021) "Transductive transfer learning based Genetic Programming for balanced and unbalanced document classification using different types of features". Applied Soft Computing, 103. doi:10.1016/j.asoc.2021.107172
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Multi-objective genetic programming for feature learning in face recognition". Applied Soft Computing, 103. doi:10.1016/j.asoc.2021.107152
  • Bahri, M., Bifet, A., Gama, J., Gomes, H. M., & Maniu, S. (2021) "Data stream analysis: Foundations, major tasks and tools". Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(3). doi:10.1002/widm.1405
  • Zhang, Y., Mei, Y., Zhang, B., & Jiang, K. (2021) "Divide-and-conquer large scale capacitated arc routing problems with route cutting off decomposition". Information Sciences, 553, 208-224. doi:10.1016/j.ins.2020.11.011
  • Xu, H., Xue, B., & Zhang, M. (2021) "A Duplication Analysis-Based Evolutionary Algorithm for Biobjective Feature Selection". IEEE Transactions on Evolutionary Computation, 25(2), 205-218. doi:10.1109/TEVC.2020.3016049
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Genetic Programming with a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification". IEEE Transactions on Cybernetics, 51(4), 1769-1783. doi:10.1109/TCYB.2020.2964566
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2021) "A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data". Soft Computing, 25(8), 5993-6012. doi:10.1007/s00500-021-05590-y
  • Zhang, Y., Mei, Y., Tang, K., & Jiang, K. (2021) "Memetic algorithm with route decomposing for periodic capacitated arc routing problem". In Victoria University of Wellington Library. doi:10.26686/wgtn.14344067
  • Rahaman, M. S., Mei, Y., Hamilton, M., & Salim, F. D. (2021) "CAPRA: A contour-based accessible path routing algorithm". In Victoria University of Wellington Library. doi:10.26686/wgtn.14343926
  • Mei, Y., Zhang, M., & Nyugen, S. (2021) "Feature selection in evolving job shop dispatching rules with genetic programming". In Victoria University of Wellington Library. doi:10.26686/wgtn.14344040
  • Mei, Y., Salim, F. D., & Li, X. (2021) "Efficient meta-heuristics for the multi-objective time-dependent orienteering problem". In Victoria University of Wellington Library. doi:10.26686/wgtn.14343968
  • Masood, A., Mei, Y., Chen, G., & Zhang, M. (2021) "Many-objective genetic programming for job-shop scheduling". In Victoria University of Wellington Library. doi:10.26686/wgtn.14344058
  • Liu, Y., Mei, Y., Zhang, M., & Zhang, Z. (2021) "Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem". In Victoria University of Wellington Library. doi:10.26686/wgtn.14343905
  • Jacobsen-Grocott, J., Mei, Y., Chen, G., & Zhang, M. (2021) "Evolving heuristics for Dynamic Vehicle Routing with Time Windows using genetic programming". In Victoria University of Wellington Library. doi:10.26686/wgtn.14343977
  • Xie, J., Mei, Y., Ernst, A. T., Li, X., & Song, A. (2021) "A bi-level optimization model for grouping constrained storage location assignment problems". In Victoria University of Wellington Library. doi:10.26686/wgtn.14337005
  • Park, J., Mei, Y., Nguyen, S., Chen, G., & Zhang, M. (2021) "An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling". In Victoria University of Wellington Library. doi:10.26686/wgtn.14337389
  • Liu, J., Mei, Y., & Li, X. (2021) "An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization". In Victoria University of Wellington Library. doi:10.26686/wgtn.14337128
  • Tran, B., Xue, B., & Zhang, M. (2021) "A New Representation in PSO for Discretization-Based Feature Selection". In Victoria University of Wellington Library. doi:10.26686/wgtn.14273582
  • Bifet, A., Ferreira, C., Gama, J., & Gomes, H. M. (2021) "Session details: Theme: Information systems: DS - Data streams track". In Proceedings of the 36th Annual ACM Symposium on Applied Computing. ACM. doi:10.1145/3462411
  • Bifet, A., Ferreira, C., Gama, J., & Gomes, H. M. (2021) "EDITORIAL MESSAGE". In Proceedings of the ACM Symposium on Applied Computing (pp. 417).
  • Mei, Y., Nguyen, S., Xue, B., & Zhang, M. (2021) "An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming". In Victoria University of Wellington Library. doi:10.26686/wgtn.14245721
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2021) "Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification". Applied Soft Computing, 101. doi:10.1016/j.asoc.2020.106989
  • Nguyen, S., Zhang, M., Alahakoon, D., & Tan, K. C. (2021) "People-Centric Evolutionary System for Dynamic Production Scheduling". IEEE Transactions on Cybernetics, 51(3), 1403-1416. doi:10.1109/TCYB.2019.2936001
  • Li, L., Fang, W., Mei, Y., & Wang, Q. (2021) "Cooperative coevolution for large-scale global optimization based on fuzzy decomposition". Soft Computing, 25(5), 3593-3608. doi:10.1007/s00500-020-05389-3
  • Cerqueira, V., Gomes, H. M., Bifet, A., & Torgo, L. (2021) "STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection". __. doi:10.48550/arxiv.2103.00903
  • Huang, G., Chen, G., & Fu, Q. (2021) "Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking". arXiv.
  • Xu, Q., Zeng, S., Zhao, F., Jiao, R., & Li, C. (2021) "On Formulating and Designing Antenna Arrays by Evolutionary Algorithms". IEEE Transactions on Antennas and Propagation, 69(2), 1118-1129. doi:10.1109/TAP.2020.3016181
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2021) "Developing Interval-Based Cost-Sensitive Classifiers by Genetic Programming for Binary High-Dimensional Unbalanced Classification [Research Frontier]". IEEE Computational Intelligence Magazine, 16(1), 84-98. doi:10.1109/MCI.2020.3039070
  • Nguyen, B. H., Xue, B., Andreae, P., & Zhang, M. (2021) "A New Binary Particle Swarm Optimization Approach: Momentum and Dynamic Balance between Exploration and Exploitation". IEEE Transactions on Cybernetics, 51(2), 589-603. doi:10.1109/TCYB.2019.2944141
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Genetic Programming with Image-Related Operators and a Flexible Program Structure for Feature Learning in Image Classification". IEEE Transactions on Evolutionary Computation, 25(1), 87-101. doi:10.1109/TEVC.2020.3002229
  • Fu, W., Xue, B., Gao, X., & Zhang, M. (2021) "Output-based transfer learning in genetic programming for document classification". Knowledge-Based Systems, 212. doi:10.1016/j.knosys.2020.106597
  • Zhang, F., Nguyen, S., Mei, Y., & Zhang, M. (2021) "Genetic Programming for Production Scheduling". Springer Singapore. doi:10.1007/978-981-16-4859-5
  • Yuan, G., Xue, B., & Zhang, M. (2021) "A Two-Stage Efficient Evolutionary Neural Architecture Search Method for Image Classification". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13031 LNAI (pp. 469-484). doi:10.1007/978-3-030-89188-6_35
  • Yu, Y., Ma, H., & Chen, G. (2021) "Achieving Multi-Objective Scheduling of Heterogeneous Workflows in Cloud through a Genetic Programming Based Approach". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 1880-1887). doi:10.1109/CEC45853.2021.9504695
  • Yang, Y., Xue, B., Jesson, L., & Zhang, M. (2021) "Genetic Programming for Symbolic Regression: A Study on Fish Weight Prediction". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 588-595). doi:10.1109/CEC45853.2021.9504963
  • Yang, Y., Xue, B., Jesson, L., Wylie, M., Zhang, M., & Wellenreuther, M. (2021) "Deep Convolutional Neural Networks for Fish Weight Prediction from Images". In International Conference Image and Vision Computing New Zealand Vol. 2021-December. doi:10.1109/IVCNZ54163.2021.9653412
  • Yang, Y., Chen, G., Ma, H., Zhang, M., & Huang, V. (2021) "Budget and SLA Aware Dynamic Workflow Scheduling in Cloud Computing with Heterogeneous Resources". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 2141-2148). doi:10.1109/CEC45853.2021.9504709
  • Yan, Z., Bi, Y., Xue, B., & Zhang, M. (2021) "Automatically Extracting Features Using Genetic Programming for Low-Quality Fish Image Classification". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 2015-2022). doi:10.1109/CEC45853.2021.9504737
  • Xue, Y., Shi, X., Zhou, H., Yang, Z., Zhang, J., Wu, C., & Xue, B. (2021) "Effects of Textured Surface Combined with Sn-Ag-Cu Coating on Tribological Properties and Friction-Induced Noise of Ti-6Al-4V Alloy". Tribology Transactions, 64(3), 562-577. doi:10.1080/10402004.2021.1881196
  • Xue, B., & Ren, W. (2021) "Reshaping Natural Resource Management in China". In Sustainable Development Goals Series (Vol. Part F2699, pp. 89-103). doi:10.1007/978-3-030-85839-1_6
  • Xu, M., Zhang, F., Mei, Y., & Zhang, M. (2021) "Genetic Programming with Archive for Dynamic Flexible Job Shop Scheduling". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 2117-2124). doi:10.1109/CEC45853.2021.9504752
  • Wu, J., Shang, L., & Gao, X. (2021) "Sentiment Time Series Calibration for Event Detection". IEEE/ACM Transactions on Audio Speech and Language Processing, 29, 2407-2420. doi:10.1109/TASLP.2021.3096653
  • Wang, Z., Zhou, Y., Li, C., Shang, L., & Xue, B. (2021) "MGEoT: A Multi-grained Ensemble Method for Time Series Classification". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13031 LNAI (pp. 397-410). doi:10.1007/978-3-030-89188-6_30
  • Wang, S., Mei, Y., & Zhang, M. (2021) "An Improved Multi-Objective Genetic Programming Hyper-Heuristic with Archive for Uncertain Capacitated Arc Routing Problem". In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. doi:10.1109/SSCI50451.2021.9660154
  • Wang, S., Mei, Y., & Zhang, M. (2021) "A Multi-Objective Genetic Programming Approach with Self-Adaptive α Dominance to Uncertain Capacitated Arc Routing Problem". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 636-643). doi:10.1109/CEC45853.2021.9504956
  • Wang, P., Xue, B., Zhang, M., & Liang, J. (2021) "A Grid-dominance based Multi-objective Algorithm for Feature Selection in Classification". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 2053-2060). doi:10.1109/CEC45853.2021.9504832
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2021) "Improved Crowding Distance in Multi-objective Optimization for Feature Selection in Classification". In Unknown Book (Vol. 12694 LNCS, pp. 489-505). doi:10.1007/978-3-030-72699-7_31
  • Viswambaran, R. A., Chen, G., Xue, B., & Nekooei, M. (2021) "Two-Stage Genetic Algorithm for Designing Long Short Term Memory (LSTM) Ensembles". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 942-949). doi:10.1109/CEC45853.2021.9504788
  • Teixeira, M., Casanova, D., Ribeiro, R., Gomes, H. M., & Schmidt, L. (2021) "A combined solution for flexible control of poultry houses". International Journal of Computer Applications in Technology, 67(2/3), 232. doi:10.1504/ijcat.2021.10045769
  • Steinmetz, D., Hartmann, S., & Ma, H. (2021) "A Conceptual Modelling Approach for the Discovery and Management of Platoon Routes". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13011 LNCS (pp. 282-296). doi:10.1007/978-3-030-89022-3_23
  • Shi, T., Ma, H., Chen, G., & Hartmann, S. (2021) "Location-Aware and Budget-Constrained Service Brokering in Multi-Cloud via Deep Reinforcement Learning". In Unknown Book (Vol. 13121 LNCS, pp. 756-764). doi:10.1007/978-3-030-91431-8_52
  • Schofield, F., & Lensen, A. (2021) "Using Genetic Programming to Find Functional Mappings for UMAP Embeddings". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 704-711). doi:10.1109/CEC45853.2021.9504848
  • Schmidt, L., Casanova, D., Ribeiro, R., Teixeira, M., & Gomes, H. M. (2021) "A combined solution for flexible control of poultry houses". International Journal of Computer Applications in Technology, 67(2-3), 232-243. doi:10.1504/IJCAT.2021.121539
  • Sadeghiram, S., Ma, H., & Chen, G. (2021) "Priority-based Selection of Individuals in Memetic Algorithms for Distributed Data-intensive Web Service compositions". IEEE Transactions on Services Computing. doi:10.1109/TSC.2021.3066322
  • Robinson, D., Chen, Q., Xue, B., Wagner, I., Price, M., Hume, P., . . . Zhang, M. (2021) "Particle Swarm Optimisation for Analysing Time-Dependent Photoluminescence Data". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 1735-1742). doi:10.1109/CEC45853.2021.9504908
  • Robinson, D., Chen, Q., Xue, B., Killeen, D., Fraser-Miller, S., Gordon, K. C., . . . Zhang, M. (2021) "Genetic Algorithm for Feature and Latent Variable Selection for Nutrient Assessment in Horticultural Products". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 272-279). doi:10.1109/CEC45853.2021.9504794
  • Pei, J., Hu, C., Liu, J., Mei, Y., & Yao, X. (2021) "Bi-Objective Splitting Delivery VRP with Loading Constraints and Restricted Access". In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. doi:10.1109/SSCI50451.2021.9659967
  • Panda, S., & Mei, Y. (2021) "Genetic Programming with Algebraic Simplification for Dynamic Job Shop Scheduling". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 1848-1855). doi:10.1109/CEC45853.2021.9505010
  • Nguyen, T. B., Browne, W. N., & Zhang, M. (2021) "Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 296-303). doi:10.1109/CEC45853.2021.9504784
  • Nguyen, H. B., Xue, B., & Zhang, M. (2021) "Automated and Efficient Sparsity-based Feature Selection via a Dual-component Vector". In IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December (pp. 833-842). doi:10.1109/ICDMW53433.2021.00107
  • Montiel, J., Halford, M., Mastelini, S. M., Bolmier, G., Sourty, R., Vaysse, R., . . . Bifet, A. (2021) "River: Machine learning for streaming data in python". Journal of Machine Learning Research, 22.
  • Mei, Y., Ardeh, M. A., & Zhang, M. (2021) "Knowledge Transfer in Genetic Programming Hyper-heuristics". In Natural Computing Series (pp. 149-169). doi:10.1007/978-3-030-72069-8_9
  • McLeay, A. J., McGhie, A., Briscoe, D., Bi, Y., Xue, B., Vennell, R., & Zhang, M. (2021) "Deep Convolutional Neural Networks with Transfer Learning for Waterline Detection in Mussel Farms". In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. doi:10.1109/SSCI50451.2021.9659987
  • Mastelini, S. M., Montiel, J., Gomes, H. M., Bifet, A., Pfahringer, B., & De Carvalho, A. C. P. L. F. (2021) "Fast and lightweight binary and multi-branch Hoeffding Tree Regressors". In IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December (pp. 380-388). doi:10.1109/ICDMW53433.2021.00053
  • Masood, A., Chen, G., & Zhang, M. (2021) "Feature Selection for Evolving Many-Objective Job Shop Scheduling Dispatching Rules with Genetic Programming". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 644-651). doi:10.1109/CEC45853.2021.9504895
  • Mandal, R., Azam, B., Verma, B., & Zhang, M. (2021) "Deep Learning Model with GA-based Visual Feature Selection and Context Integration". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 288-295). doi:10.1109/CEC45853.2021.9504753
  • Madukwe, K. J., Gao, X., & Xue, B. (2021) "What Emotion Is Hate? Incorporating Emotion Information into the Hate Speech Detection Task". In Unknown Book (Vol. 13032 LNAI, pp. 273-286). doi:10.1007/978-3-030-89363-7_21
  • MacLachlan, J., & Mei, Y. (2021) "Look-Ahead Genetic Programming for Uncertain Capacitated Arc Routing Problem". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 1872-1879). doi:10.1109/CEC45853.2021.9504785
  • Londt, T., Gao, X., & Andreae, P. (2021) "Evolving Character-Level DenseNet Architectures Using Genetic Programming". In Unknown Book (Vol. 12694 LNCS, pp. 665-680). doi:10.1007/978-3-030-72699-7_42
  • Li, A. D., Xue, B., & Zhang, M. (2021) "A Forward Search Inspired Particle Swarm Optimization Algorithm for Feature Selection in Classification". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 786-793). doi:10.1109/CEC45853.2021.9504949
  • Lensen, A. (2021) "Mining Feature Relationships in Data". In Unknown Book (Vol. 12691 LNCS, pp. 247-262). doi:10.1007/978-3-030-72812-0_16
  • Huang, Z., Mei, Y., & Zhang, M. (2021) "Investigation of Linear Genetic Programming for Dynamic Job Shop Scheduling". In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. doi:10.1109/SSCI50451.2021.9660091
  • Huang, J., Xue, B., Sun, Y., & Zhang, M. (2021) "A Flexible Variable-length Particle Swarm Optimization Approach to Convolutional Neural Network Architecture Design". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 934-941). doi:10.1109/CEC45853.2021.9504716
  • Hong, W. C., Niu, D., Xu, Y., Zhang, M., & Singh, P. K. (2021) "Advanced Intelligent Technologies in Energy Forecasting and Economical Applications". Mathematical Problems in Engineering, 2021. doi:10.1155/2021/9865857
  • Ha, T., & Gao, X. (2021) "Fake News Detection Using Multiple-View Text Representation". In Unknown Book (Vol. 13032 LNAI, pp. 100-112). doi:10.1007/978-3-030-89363-7_8
  • Ghosh, A., Xue, B., & Zhang, M. (2021) "Binary Differential Evolution based Feature Selection Method with Mutual Information for Imbalanced Classification Problems". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 794-801). doi:10.1109/CEC45853.2021.9504882
  • Demir, K., Nguyen, B. H., Xue, B., & Zhang, M. (2021) "Multi-objective Multi-label Feature Selection with an Aggregated Performance Metric and Dominance-based Initialisation". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 760-767). doi:10.1109/CEC45853.2021.9504960
  • Demir, K., Nguyen, B. H., Xue, B., & Zhang, M. (2021) "Multi-objective Feature Selection with a Sparsity-based Objective Function and Gradient Local Search for Multi-label Classification". In IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December (pp. 823-832). doi:10.1109/ICDMW53433.2021.00106
  • Currie, F., Mei, Y., Zhang, M., Wellenreuther, M., & Jesson, L. (2021) "An Investigation on Multi-Objective Fish Breeding Program Design". In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. doi:10.1109/SSCI50451.2021.9659936
  • Costa, J. G. C., Mei, Y., & Zhang, M. (2021) "Learning Penalisation Criterion of Guided Local Search for Large Scale Vehicle Routing Problem". In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. doi:10.1109/SSCI50451.2021.9659939
  • Costa, J. G. C., Mei, Y., & Zhang, M. (2021) "Learning Initialisation Heuristic for Large Scale Vehicle Routing Problem with Genetic Programming". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 1864-1871). doi:10.1109/CEC45853.2021.9504938
  • Costa, J. G. C., Mei, Y., & Zhang, M. (2021) "An Evolutionary Hyper-Heuristic Approach to the Large Scale Vehicle Routing Problem". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 2109-2116). doi:10.1109/CEC45853.2021.9504818
  • Chanajitt, R., Pfahringer, B., & Gomes, H. M. (2021) "Combining Static and Dynamic Analysis to Improve Machine Learning-based Malware Classification". In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021. doi:10.1109/DSAA53316.2021.9564144
  • Castillo, P. A., Laredo, J. L. J., Iacca, G., Bucur, D., Cotta, C., Fernández, P., . . . Merelo Guervós, J. J. (2021) "Preface (Vol". 12694 LNCS).
  • Buchanan, C., Bi, Y., Xue, B., Vennell, R., Childerhouse, S., Pine, M. K., . . . Zhang, M. (2021) "Deep Convolutional Neural Networks for Detecting Dolphin Echolocation Clicks". In International Conference Image and Vision Computing New Zealand Vol. 2021-December. doi:10.1109/IVCNZ54163.2021.9653250
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Random Forest-Assisted GP for Feature Learning". In Adaptation, Learning, and Optimization (Vol. 24, pp. 207-226). doi:10.1007/978-3-030-65927-1_9
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Preface". In Unknown Book (Vol. 24, pp. ix-x). doi:10.1016/B978-0-12-809952-0.05001-9
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Multi-layer Representation for Binary Image Classification". In Adaptation, Learning, and Optimization (Vol. 24, pp. 75-95). doi:10.1007/978-3-030-65927-1_4
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Introduction". In Adaptation, Learning, and Optimization (Vol. 24, pp. 1-10). doi:10.1007/978-3-030-65927-1_1
  • Bi, Y., Xue, B., & Zhang, M. (2021) "GP with Image-Related Operators for Feature Learning". In Adaptation, Learning, and Optimization (Vol. 24, pp. 145-177). doi:10.1007/978-3-030-65927-1_7
  • Bi, Y., Xue, B., & Zhang, M. (2021) "GP with Image Descriptors for Learning Global and Local Features". In Adaptation, Learning, and Optimization (Vol. 24, pp. 117-143). doi:10.1007/978-3-030-65927-1_6
  • Bi, Y., Xue, B., & Zhang, M. (2021) "GP for Simultaneous Feature Learning and Ensemble Learning". In Adaptation, Learning, and Optimization (Vol. 24, pp. 179-205). doi:10.1007/978-3-030-65927-1_8
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Genetic Programming for Image Classification". Springer International Publishing. doi:10.1007/978-3-030-65927-1
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Evolutionary Deep Learning Using GP with Convolution Operators". In Adaptation, Learning, and Optimization (Vol. 24, pp. 97-115). doi:10.1007/978-3-030-65927-1_5
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Evolutionary Computation and Genetic Programming". In Adaptation, Learning, and Optimization (Vol. 24, pp. 49-74). doi:10.1007/978-3-030-65927-1_3
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Conclusions and Future Directions". In Adaptation, Learning, and Optimization (Vol. 24, pp. 227-237). doi:10.1007/978-3-030-65927-1_10
  • Bi, Y., Xue, B., & Zhang, M. (2021) "Computer Vision and Machine Learning". In Adaptation, Learning, and Optimization (Vol. 24, pp. 11-48). doi:10.1007/978-3-030-65927-1_2
  • Ariadi, A., Shi, T., Ma, H., Da Silva, A. S., & Hartmann, S. (2021) "A Graph Database Supported GA-Based Approach to Social Network Analysis". In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. doi:10.1109/SSCI50451.2021.9659546
  • Ardeh, M. A., Mei, Y., & Zhang, M. (2021) "Surrogate-Assisted Genetic Programming with Diverse Transfer for the Uncertain Capacitated Arc Routing Problem". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 628-635). doi:10.1109/CEC45853.2021.9504817
  • Andersen, H., Stevenson, S., Ha, T., Gao, X., & Xue, B. (2021) "Evolving Neural Networks for Text Classification using Genetic Algorithm-based Approaches". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 1241-1248). doi:10.1109/CEC45853.2021.9504920
  • Andersen, H., Lensen, A., & Xue, B. (2021) "Genetic Programming for Evolving Similarity Functions Tailored to Clustering Algorithms". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 688-695). doi:10.1109/CEC45853.2021.9504855
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2021) "GP with a Hybrid Tree-vector Representation for Instance Selection and Symbolic Regression on Incomplete Data". In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 604-611). doi:10.1109/CEC45853.2021.9504767
  • Abbasi, M. S., Al-Sahaf, H., & Welch, I. (2021) "Automated Behavior-based Malice Scoring of Ransomware Using Genetic Programming". In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. doi:10.1109/SSCI50451.2021.9660009
  • Bi, Y., Xue, B., & Zhang, M. (2020) "Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning". __. doi:10.48550/arxiv.2012.09444
  • Bernardo, A., Gomes, H. M., Montiel, J., Pfahringer, B., Bifet, A., & Valle, E. D. (2020) "C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams". In Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 483-492). doi:10.1109/BigData50022.2020.9377768
  • Montiel, J., Halford, M., Mastelini, S. M., Bolmier, G., Sourty, R., Vaysse, R., . . . Bifet, A. (2020) "River: machine learning for streaming data in Python". __. doi:10.48550/arxiv.2012.04740
  • Londt, T., Gao, X., Xue, B., & Andreae, P. (2020) "Evolving Character-level Convolutional Neural Networks for Text Classification". Retrieved from http://arxiv.org/abs/2012.
  • Londt, T., Gao, X., Xue, B., & Andreae, P. (2020) "Evolving Character-level Convolutional Neural Networks for Text Classification". __. doi:10.48550/arxiv.2012.02223
  • Londt, T., Gao, X., & Andreae, P. (2020) "Evolving Character-Level DenseNet Architectures using Genetic Programming". __. doi:10.48550/arxiv.2012.02327
  • Zhao, W., Sun, Y., & Xue, B. (2020) "Improved Binary Particle Swarm optimization with Evolutionary Population Dynamic for Key Oncogene Selection". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 897-904). doi:10.1109/SSCI47803.2020.9308540
  • Wang, S., Mei, Y., & Zhang, M. (2020) "Towards Interpretable Routing Policy: A Two Stage Multi-Objective Genetic Programming Approach with Feature Selection for Uncertain Capacitated Arc Routing Problem". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2399-2406). doi:10.1109/SSCI47803.2020.9308588
  • Viswambaran, R. A., Chen, G., Xue, B., & Nekooei, M. (2020) "Evolutionary Design of Long Short Term Memory (LSTM) Ensemble". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2692-2698). doi:10.1109/SSCI47803.2020.9308393
  • Sadeghiram, S., Ma, H., & Chen, G. (2020) "A User-Preference Driven Lexicographic Approach for Multi-Objective Distributed Web Service Composition". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 791-797). doi:10.1109/SSCI47803.2020.9308222
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2020) "Genetic programming for high-dimensional imbalanced classification with a new fitness function and program reuse mechanism". Soft Computing, 24(23), 18021-18038. doi:10.1007/s00500-020-05056-7
  • McGhie, A., Xue, B., & Zhang, M. (2020) "GPCNN: Evolving Convolutional Neural Networks using Genetic Programming". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2684-2691). doi:10.1109/SSCI47803.2020.9308390
  • Madukwe, K. J., Gao, X., & Xue, B. (2020) "Dependency-based embedding for distinguishing between hate speech and offensive language". In Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020 (pp. 860-868). doi:10.1109/WIIAT50758.2020.00132
  • Madukwe, K. J., Gao, X., & Xue, B. (2020) "A GA-Based Approach to Fine-Tuning BERT for Hate Speech Detection". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2821-2828). doi:10.1109/SSCI47803.2020.9308419
  • Ma, J., & Gao, X. (2020) "Designing genetic programming classifiers with feature selection and feature construction". Applied Soft Computing Journal, 97. doi:10.1016/j.asoc.2020.106826
  • Escott, K. R., Ma, H., & Chen, G. (2020) "A Genetic Programming Hyper-Heuristic Approach to Design High-Level Heuristics for Dynamic Workflow Scheduling in Cloud". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 3141-3148). doi:10.1109/SSCI47803.2020.9308261
  • Costa, J. G. C., Mei, Y., & Zhang, M. (2020) "Adaptive Search Space through Evolutionary Hyper-Heuristics for the Large-Scale Vehicle Routing Problem". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2415-2422). doi:10.1109/SSCI47803.2020.9308239
  • Cassales, G., Gomes, H., Bifet, A., Pfahringer, B., & Senger, H. (2020) "Improving parallel performance of ensemble learners for streaming data through data locality with mini-batching". In Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 (pp. 138-146). doi:10.1109/HPCC-SmartCity-DSS50907.2020.00018
  • Cagnoni, S., Al-Sahaf, H., Sun, Y., Xue, B., & Zhang, M. (2020) "Special Issue on Evolutionary Computer Vision, Image Processing and Pattern Recognition". Applied Soft Computing, 97, 106675. doi:10.1016/j.asoc.2020.106675
  • Ardeh, M. A., Mei, Y., & Zhangz, M. (2020) "Diversity-driven Knowledge Transfer for GPHH to Solve Uncertain Capacitated Arc Routing Problem". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2407-2414). doi:10.1109/SSCI47803.2020.9308501
  • Ardeh, M. A., Mei, Y., & Zhang, M. (2020) "A GPHH with Surrogate-assisted Knowledge Transfer for Uncertain Capacitated Arc Routing Problem". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2786-2793). doi:10.1109/SSCI47803.2020.9308398
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020) "GP-based Feature Selection and Weighted KNN-based Instance Selection for Symbolic Regression with Incomplete Data". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 905-912). doi:10.1109/SSCI47803.2020.9308382
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020) "Data Imputation for Symbolic Regression with Missing Values: A Comparative Study". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2093-2100). doi:10.1109/SSCI47803.2020.9308216
  • Albuquerque, I. M. R., Nguyen, B. H., Xue, B., & Zhang, M. (2020) "A Novel Genetic Algorithm Approach to Simultaneous Feature Selection and Instance Selection". In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 616-623). doi:10.1109/SSCI47803.2020.9308307
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., & Li, J. (2020) "A dictionary-based oversampling approach to clinical document classification on small and imbalanced dataset". In Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020 (pp. 357-364). doi:10.1109/WIIAT50758.2020.00051
  • Yuan, G., Xue, B., & Zhang, M. (2020) "A Graph-Based Approach to Automatic Convolutional Neural Network Construction for Image Classification". In International Conference Image and Vision Computing New Zealand Vol. 2020-November. doi:10.1109/IVCNZ51579.2020.9290492
  • Ceschin, F., Botacin, M., Lüders, G., Gomes, H. M., Oliveira, L., & Gregio, A. (2020) "No Need to Teach New Tricks to Old Malware: Winning an Evasion Challenge with XOR-based Adversarial Samples". In ACM International Conference Proceeding Series (pp. 13-22). doi:10.1145/3433667.3433669
  • Shi, T., Ma, H., & Chen, G. (2020) "Seeding-based multi-objective evolutionary algorithms for multi-cloud composite applications deployment". In Proceedings - 2020 IEEE 13th International Conference on Services Computing, SCC 2020 (pp. 240-247). doi:10.1109/SCC49832.2020.00039
  • Sadeghiram, S., Ma, H., & Chen, G. (2020) "A distance-based genetic algorithm for robust data-intensive web service composition in dynamic bandwidth environment". In Proceedings - 2020 IEEE 13th International Conference on Services Computing, SCC 2020 (pp. 248-255). doi:10.1109/SCC49832.2020.00040
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2020) "Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling". In Victoria University of Wellington Library. doi:10.26686/wgtn.13158314
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2020) "Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling". In Victoria University of Wellington Library. doi:10.26686/wgtn.13158317
  • Nguyen, S., Mei, Y., Xue, B., & Zhang, M. (2020) "A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules". In Victoria University of Wellington Library. doi:10.26686/wgtn.13158296
  • Ceschin, F., Botacin, M., Bifet, A., Pfahringer, B., Oliveira, L. S., Gomes, H. M., & Grégio, A. (2020) "Machine Learning (In) Security: A Stream of Problems". __. doi:10.48550/arxiv.2010.16045
  • Nguyen, S., Mei, Y., & Zhang, M. (2020) "Genetic programming for production scheduling: a survey with a unified framework". In Victoria University of Wellington Library. doi:10.26686/wgtn.13152413
  • Iqbal, M., Xue, B., Al-Sahaf, H., & Zhang, M. (2020) "Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification". In Victoria University of Wellington Library. doi:10.26686/wgtn.13152290
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2020) "Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming". In Victoria University of Wellington Library. doi:10.26686/wgtn.13150949
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2020) "A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem". In Victoria University of Wellington Library. doi:10.26686/wgtn.13146524
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2020) "Evolving Scheduling Heuristics via Genetic Programming with Feature Selection in Dynamic Flexible Job Shop Scheduling". __. doi:10.1109/TCYB.2020.3024849
  • Manapragada, C., Gomes, H. M., Salehi, M., Bifet, A., & Webb, G. I. (2020) "An Eager Splitting Strategy for Online Decision Trees". __. doi:10.48550/arxiv.2010.10935
  • Mostofi, A., JAIN, V., & MEI, Y. I. (2020) "A Model for Pricing the License of Brand-Name Drugs Using Cooperative Game Theory". In Victoria University of Wellington Library. doi:10.26686/wgtn.13096184
  • Fletcher, S., Verma, B., & Zhang, M. (2020) "A non-specialized ensemble classifier using multi-objective optimization". Neurocomputing, 409, 93-102. doi:10.1016/j.neucom.2020.05.029
  • Shi, T., Ma, H., Chen, G., & Hartmann, S. (2020) "Location-Aware and Budget-Constrained Application Replication and Deployment in Multi-Cloud Environment". In Proceedings - 2020 IEEE 13th International Conference on Web Services, ICWS 2020 (pp. 110-117). doi:10.1109/ICWS49710.2020.00022
  • Lin, J., Liu, H. L., Xue, B., Zhang, M., & Gu, F. (2020) "Multiobjective Multitasking Optimization Based on Incremental Learning". IEEE Transactions on Evolutionary Computation, 24(5), 824-838. doi:10.1109/TEVC.2019.2962747
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2020) "Evolving Scheduling Heuristics via Genetic Programming with Feature Selection in Dynamic Flexible Job Shop Scheduling". In Victoria University of Wellington Library. doi:10.26686/wgtn.12986303
  • Xue, B., Yang, Z., Shi, X., Zhou, H., Lu, G., Xue, Y., . . . Wu, C. (2020) "Study on the Lubrication Mechanism of Titanium Alloys with Surface Dimples Filled with Sn-Ag-Cu and TiC under Dry Sliding Friction". Journal of Materials Engineering and Performance, 29(9), 5776-5786. doi:10.1007/s11665-020-05092-2
  • Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Lv, J. (2020) "Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification". IEEE Transactions on Cybernetics, 50(9), 3840-3854. doi:10.1109/TCYB.2020.2983860
  • Lensen, A., Zhang, M., & Xue, B. (2020) "Multi-objective genetic programming for manifold learning: balancing quality and dimensionality". Genetic Programming and Evolvable Machines, 21(3), 399-431. doi:10.1007/s10710-020-09375-4
  • Grzenda, M., Gomes, H. M., & Bifet, A. (2020) "Delayed labelling evaluation for data streams". Data Mining and Knowledge Discovery, 34(5), 1237-1266. doi:10.1007/s10618-019-00654-y
  • Chen, B., Xu, Z., Liu, Y., Xue, B., & Ma, W. (2020) "Tribological Performance and Tribofilm Evolution of TiAl Matrix Composites with Silver and Titanium Diboride at Elevated Temperatures". Journal of Materials Engineering and Performance, 29(9), 5655-5662. doi:10.1007/s11665-020-04936-1
  • Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., . . . Xue, B. (2020) "A cultivated planet in 2010 - Part 1: The global synergy cropland map". Earth System Science Data, 12(3), 1913-1928. doi:10.5194/essd-12-1913-2020
  • Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Tan, K. C. (2020) "A Survey on Evolutionary Neural Architecture Search". __. doi:10.48550/arxiv.2008.10937
  • Liu, Y., Sun, Y., Xue, B., & Zhang, M. (2020) "Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising". __. doi:10.48550/arxiv.2008.06634
  • Shi, T., Ma, H., Chen, G., & Hartmann, S. (2020) "Location-Aware and Budget-Constrained Service Deployment for Composite Applications in Multi-Cloud Environment". IEEE Transactions on Parallel and Distributed Systems, 31(8), 1954-1969. doi:10.1109/TPDS.2020.2981306
  • Machová, K., Mikula, M., Gao, X., & Mach, M. (2020) "Lexicon-based sentiment analysis using particle swarm optimization". Electronics (Switzerland), 9(8), 1-22. doi:10.3390/electronics9081317
  • Jabeen, S., Gao, X., & Andreae, P. (2020) "Semantic association computation: a comprehensive survey". Artificial Intelligence Review, 53(6), 3849-3899. doi:10.1007/s10462-019-09781-w
  • Hancer, E., Xue, B., & Zhang, M. (2020) "A survey on feature selection approaches for clustering". Artificial Intelligence Review, 53(6), 4519-4545. doi:10.1007/s10462-019-09800-w
  • Da Silva, A. S., Ma, H., Mei, Y., & Zhang, M. (2020) "A Survey of evolutionary computation for web service composition: A technical perspective". IEEE Transactions on Emerging Topics in Computational Intelligence, 4(4), 538-554. doi:10.1109/TETCI.2020.2969213
  • Zhang, M., & Cagnoni, S. (2020) "Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition". In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 1343-1372). doi:10.1145/3377929.3389856
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2020) "A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programming". In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 107-108). doi:10.1145/3377929.3389934
  • Xue, B., & Zhang, M. (2020) "Evolutionary computation for feature selection and feature construction". In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 1283-1312). doi:10.1145/3377929.3389857
  • Shi, T., Ma, H., & Chen, G. (2020) "Divide and conquer: Seeding strategies for multi-objective multi-cloud composite applications deployment". In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 317-318). doi:10.1145/3377929.3389927
  • Sadeghiram, S., Ma, H., & Chen, G. (2020) "QoS-constrained multi-objective distributed data-intensive web service composition - NSGA-II with repair method". In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 105-106). doi:10.1145/3377929.3389977
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2020) "A genetic programming method for classifier construction and cost learning in high-dimensional unbalanced classification". In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 149-150). doi:10.1145/3377929.3389955
  • Bi, Y., Xue, B., & Zhang, M. (2020) "Automatically extracting features for face classification using multi-objective genetic programming". In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 117-118). doi:10.1145/3377929.3389989
  • Andersen, H., Gao, X., Xue, B., & Zhang, M. (2020) "Evolving network structures for text classification using genetic algorithms". In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 109-110). doi:10.1145/3377929.3390068
  • Ain, Q. U., Xue, B., Al-Sahaf, H., & Zhang, M. (2020) "Multi-tree Genetic Programming with A New Fitness Function for Melanoma Detection". In Victoria University of Wellington Library. doi:10.26686/wgtn.12616751.v1
  • Wang, B., Xue, B., & Zhang, M. (2020) "Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification". __. doi:10.48550/arxiv.2007.01556
  • Wang, S., Mei, Y., & Zhang, M. (2020) "A Multi-Objective Genetic Programming Hyper-Heuristic Approach to Uncertain Capacitated Arc Routing Problems". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185890
  • Wang, B., Xue, B., & Zhang, M. (2020) "Particle Swarm optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185541
  • Viswambaran, R. A., Chen, G., Xue, B., & Nekooei, M. (2020) "Evolving Deep Recurrent Neural Networks Using A New Variable-Length Genetic Algorithm". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185851
  • Schofield, F., & Lensen, A. (2020) "Evolving Simpler Constructed Features for Clustering Problems with Genetic Programming". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185575
  • Peng, Y., Chen, G., & Zhang, M. (2020) "Effective Linear Policy Gradient Search through Primal-Dual Approximation". In Proceedings of the International Joint Conference on Neural Networks. doi:10.1109/IJCNN48605.2020.9206831
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2020) "A Threshold-free Classification Mechanism in Genetic Programming for High-dimensional Unbalanced Classification". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185503
  • Liu, Y., Sun, Y., Xue, B., & Zhang, M. (2020) "Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising". In Proceedings of the International Joint Conference on Neural Networks. doi:10.1109/IJCNN48605.2020.9207509
  • Jackson, J., & Mei, Y. (2020) "Genetic Programming Hyper-heuristic with Cluster Awareness for Stochastic Team Orienteering Problem with Time Windows". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185911
  • Grzenda, M., Gomes, H. M., & Bifet, A. (2020) "Performance measures for evolving predictions under delayed labelling classification". In Proceedings of the International Joint Conference on Neural Networks. doi:10.1109/IJCNN48605.2020.9207256
  • Gomes, H. M., Montiel, J., Mastelini, S. M., Pfahringer, B., & Bifet, A. (2020) "On Ensemble Techniques for Data Stream Regression". In Proceedings of the International Joint Conference on Neural Networks. doi:10.1109/IJCNN48605.2020.9206756
  • Gao, G., Mei, Y., Xin, B., Jia, Y. H., & Browne, W. (2020) "A Memetic Algorithm for the Task Allocation Problem on Multi-robot Multi-point Dynamic Aggregation Missions". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185647
  • Fan, Q., Xue, B., & Zhang, M. (2020) "A Region Adaptive Image Classification Approach Using Genetic Programming". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185908
  • Evans, B., Xue, B., & Zhang, M. (2020) "An Adaptive and near Parameter-free Evolutionary Computation Approach towards True Automation in AutoML". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185770
  • Demir, K., Nguyen, B. H., Xue, B., & Zhang, M. (2020) "A Decomposition based Multi-objective Evolutionary Algorithm with ReliefF based Local Search and Solution Repair Mechanism for Feature Selection". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185590
  • Costa, J. G. C., Mei, Y., & Zhang, M. (2020) "Cluster-based Hyper-Heuristic for Large-Scale Vehicle Routing Problem". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185831
  • Chen, K., Xue, B., Zhang, M., & Zhou, F. (2020) "Hybridising Particle Swarm optimisation with Differential Evolution for Feature Selection in Classification". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185533
  • Bi, Y., Xue, B., & Zhang, M. (2020) "Genetic Programming-Based Feature Learning for Facial Expression Classification". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185491
  • Bahri, M., Gomes, H. M., Bifet, A., & Maniu, S. (2020) "CS-ARF: Compressed Adaptive Random Forests for Evolving Data Stream Classification". In Proceedings of the International Joint Conference on Neural Networks. doi:10.1109/IJCNN48605.2020.9207188
  • Ardeh, M. A., Mei, Y., & Zhang, M. (2020) "Genetic Programming Hyper-Heuristics with Probabilistic Prototype Tree Knowledge Transfer for Uncertain Capacitated Arc Routing Problems". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185714
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020) "Multi-Tree Genetic Programming-based Transformation for Transfer Learning in Symbolic Regression with Highly Incomplete Data". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185670
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020) "Genetic Programming with Noise Sensitivity for Imputation Predictor Selection in Symbolic Regression with Incomplete Data". In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. doi:10.1109/CEC48606.2020.9185526
  • Adnan, M., Gao, X., Bai, X., Newbern, E., Sherwood, J., Jones, N., . . . Gao, W. (2020) "Potential early identification of a large campylobacter outbreak using alternative surveillance data sources: Autoregressive modelling and spatiotemporal clustering". JMIR Public Health and Surveillance, 6(3). doi:10.2196/18281
  • Xu, B., Mei, Y., Wang, Y., Ji, Z., & Zhang, M. (2020) "Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling". In Victoria University of Wellington Library. doi:10.26686/wgtn.12585197
  • Xu, H., Xue, B., & Zhang, M. (2020) "Segmented initialization and offspring modification in evolutionary algorithms for bi-objective feature selection". In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 444-452). doi:10.1145/3377930.3390192
  • Raymond, C., Chen, Q., Xue, B., & Zhang, M. (2020) "Adaptive weighted splines: A new representation to genetic programming for symbolic regression". In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 1003-1011). doi:10.1145/3377930.3390244
  • O'Neill, D., Xue, B., & Zhang, M. (2020) "Neural architecture search for sparse DenseNets with dynamic compression". In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 386-394). doi:10.1145/3377930.3390178
  • Nguyen, T. B., Browne, W. N., & Zhang, M. (2020) "Relatedness measures to aid the transfer of building blocks among multiple tasks". GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 377-385. doi:10.1145/3377930.3390169
  • Liu, Y., Browne, W. N., & Xue, B. (2020) "Absumption and subsumption based learning classifier systems". In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 368-376). doi:10.1145/3377930.3389813
  • Jia, Y. H., Mei, Y., & Zhang, M. (2020) "A memetic level-based learning swarm optimizer for large-scale water distribution network optimization". In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 1107-1115). doi:10.1145/3377930.3389828
  • Evans, B. P., Xue, B., & Zhang, M. (2020) "Improving generalisation of AutoML systems with dynamic fitness evaluations". In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 324-332). doi:10.1145/3377930.3389805
  • Chen, Q., Xue, B., & Zhang, M. (2020) "Improving symbolic regression based on correlation between residuals and variables". In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 922-930). doi:10.1145/3377930.3390161
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020) "Multi-tree genetic programming for feature construction-based domain adaptation in symbolic regression with incomplete data". In GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 913-921). doi:10.1145/3377930.3390160
  • Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., . . . Zhang, M. (2020) "A Survey on Evolutionary Machine Learning". __. doi:10.26686/wgtn.12493928
  • Alvarez, I. M., Nguyen, T. B., Browne, W. N., & Zhang, M. (2020) "A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems". Retrieved from http://arxiv.org/abs/2006.
  • Alvarez, I. M., Nguyen, T. B., Browne, W. N., & Zhang, M. (2020) "A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems". __. doi:10.48550/arxiv.2006.01415
  • Nekooei, S. M., & Chen, G. (2020) "Cooperative Coevolution Design of Multilevel Fuzzy Logic Controllers for Media Access Control in Wireless Body Area Networks". IEEE Transactions on Emerging Topics in Computational Intelligence, 4(3), 336-350. doi:10.1109/TETCI.2018.2877787
  • Li, A. D., Xue, B., & Zhang, M. (2020) "Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection". Information Sciences, 523, 245-265. doi:10.1016/j.ins.2020.03.032
  • Huang, V., Chen, G., Zhang, P., Li, H., Hu, C., Pan, T., & Fu, Q. (2020) "A Scalable Approach to SDN Control Plane Management: High Utilization Comes with Low Latency". IEEE Transactions on Network and Service Management, 17(2), 682-695. doi:10.1109/TNSM.2020.2973222
  • He, Y., Schnabel, M. A., & Mei, Y. (2020) "A novel methodology for architectural wind environment study by integrating CFD simulation, multiple parametric tools and evaluation criteria". Building Simulation, 13(3), 609-625. doi:10.1007/s12273-019-0591-8
  • De Lorenzo, A., Bartoli, A., Castelli, M., Medvet, E., & Xue, B. (2020) "Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence". Genetic Programming and Evolvable Machines, 21(1-2), 181-204. doi:10.1007/s10710-019-09363-3
  • Ma, J., & Gao, X. (2020) "A filter-based feature construction and feature selection approach for classification using Genetic Programming". Knowledge-Based Systems, 196. doi:10.1016/j.knosys.2020.105806
  • Nguyen, T. B., Browne, W. N., & Zhang, M. (2020) "Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks". __. doi:10.48550/arxiv.2005.03947
  • Chen, G. (2020) "Merging Deterministic Policy Gradient Estimations with Varied Bias-Variance Tradeoff for Effective Deep Reinforcement Learning". Retrieved from https://arxiv.org/abs/1911.
  • Tan, B., Ma, H., & Mei, Y. (2020) "A NSGA-II-based Approach for Multi-objective Micro-service Allocation in Container-based Clouds". In Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020 (pp. 282-289). doi:10.1109/CCGrid49817.2020.00-65
  • Nguyen, B. H., Xue, B., & Zhang, M. (2020) "A survey on swarm intelligence approaches to feature selection in data mining". Swarm and Evolutionary Computation, 54. doi:10.1016/j.swevo.2020.100663
  • Huang, Z., Zhong, J., Feng, L., Mei, Y., & Cai, W. (2020) "A fast parallel genetic programming framework with adaptively weighted primitives for symbolic regression". Soft Computing, 24(10), 7523-7539. doi:10.1007/s00500-019-04379-4
  • Bi, Y., Xue, B., & Zhang, M. (2020) "An effective feature learning approach using genetic programming with image descriptors for image classification". IEEE Computational Intelligence Magazine, 15(2), 65-77. doi:10.1109/MCI.2020.2976186
  • Nguyen, T. B., Browne, W. N., & Zhang, M. (2020) "Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions". Retrieved from http://arxiv.org/abs/2004.
  • Nguyen, T. B., Browne, W. N., & Zhang, M. (2020) "Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions". __. doi:10.48550/arxiv.2004.10978
  • Chen, K., Xue, B., Zhang, M., & Zhou, F. (2020) "Novel chaotic grouping particle swarm optimization with a dynamic regrouping strategy for solving numerical optimization tasks". Knowledge-Based Systems, 194. doi:10.1016/j.knosys.2020.105568
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2020) "Completely Automated CNN Architecture Design Based on Blocks". IEEE Transactions on Neural Networks and Learning Systems, 31(4), 1242-1254. doi:10.1109/TNNLS.2019.2919608
  • Sun, Y., Wang, H., Xue, B., Jin, Y., Yen, G. G., & Zhang, M. (2020) "Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor". IEEE Transactions on Evolutionary Computation, 24(2), 350-364. doi:10.1109/TEVC.2019.2924461
  • Sun, Y., Ren, Z., Yen, G. G., Xue, B., Zhang, M., & Lv, J. (2020) "ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures". __. doi:10.48550/arxiv.2002.10233
  • Adnan, M., Gao, X., Bai, X., Newbern, E., Sherwood, J., Jones, N., . . . Gao, W. (2020) "Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering (Preprint)". __. doi:10.2196/preprints.18281
  • Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., . . . Xue, B. (2020) "A cultivated planet in 2010: 1". the global synergy cropland map. doi:10.5194/essd-2020-12
  • Zhang, M., & Tan, K. C. (2020) "Conference Report on 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019) [Conference Reports]". IEEE Computational Intelligence Magazine, 15(1), 4-5. doi:10.1109/MCI.2019.2937595
  • Nguyen, B. H., Xue, B., Andreae, P., Ishibuchi, H., & Zhang, M. (2020) "Multiple Reference Points-Based Decomposition for Multiobjective Feature Selection in Classification: Static and Dynamic Mechanisms". IEEE Transactions on Evolutionary Computation, 24(1), 170-184. doi:10.1109/TEVC.2019.2913831
  • Evans, B. P., Xue, B., & Zhang, M. (2020) "An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML". __. doi:10.48550/arxiv.2001.10178
  • Lensen, A., Xue, B., & Zhang, M. (2020) "Genetic Programming for Evolving a Front of Interpretable Models for Data Visualisation". __. doi:10.48550/arxiv.2001.09578
  • Evans, B. P., Xue, B., & Zhang, M. (2020) "Improving generalisation of AutoML systems with dynamic fitness evaluations". __. doi:10.48550/arxiv.2001.08842
  • Lensen, A., Zhang, M., & Xue, B. (2020) "Multi-Objective Genetic Programming for Manifold Learning: Balancing Quality and Dimensionality". __. doi:10.48550/arxiv.2001.01331
  • Zhang, Q., Cao, M., Zhang, F., Liu, J., & Li, X. (2020) "Effects of corporate social responsibility on customer satisfaction and organizational attractiveness: A signaling perspective". Business Ethics, 29(1), 20-34. doi:10.1111/beer.12243
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2020) "Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12101 LNCS (pp. 262-278). doi:10.1007/978-3-030-44094-7_17
  • Zhang, F., Mei, Y., Nguyen, S., & Zhang, M. (2020) "Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12102 LNCS (pp. 214-230). doi:10.1007/978-3-030-43680-3_14
  • Xu, B., Mei, Y., Wang, Y., Ji, Z., & Zhang, M. (2020) "Genetic programming with delayed routing formultiobjective dynamic flexible job shop scheduling". Evolutionary Computation, 29(1), 75-105. doi:10.1162/evco_a_00273
  • Wang, B., Xue, B., & Zhang, M. (2020) "Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches". In Natural Computing Series (pp. 155-184). doi:10.1007/978-981-15-3685-4_6
  • Tan, B., Ma, H., & Mei, Y. (2020) "A Group Genetic Algorithm for Resource Allocation in Container-Based Clouds". In Unknown Book (Vol. 12102 LNCS, pp. 180-196). doi:10.1007/978-3-030-43680-3_12
  • Sadeghiram, S., Ma, H., & Chen, G. (2020) "A Novel Repair-Based Multi-objective Algorithm for QoS-Constrained Distributed Data-Intensive Web Service Composition". In Unknown Book (Vol. 12342 LNCS, pp. 489-502). doi:10.1007/978-3-030-62005-9_35
  • Masood, A., Chen, G., Mei, Y., Al-Sahaf, H., & Zhang, M. (2020) "A Fitness-based Selection Method for Pareto Local Search for Many-Objective Job Shop Scheduling". In Proceedings of 2020 IEEE Congress on Evolutionary Computation (CEC 2020) (pp. 1-8). IEEE. doi:10.1109/CEC48606.2020.9185881
  • Madukwe, K., Gao, X., & Xue, B. (2020) "In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets". In Proceedings of the Fourth Workshop on Online Abuse and Harms. Association for Computational Linguistics. doi:10.18653/v1/2020.alw-1.18
  • Kumar, S., Gao, X., & Welch, I. (2020) "Retraction Note to: Co-clustering for Dual Topic Models". In Lecture Notes in Computer Science (pp. C1). Springer International Publishing. doi:10.1007/978-3-319-50127-7_67
  • Haseeb, J., Mansoori, M., Al-Sahaf, H., & Welch, I. (2020) "IoT Attacks: Features Identification and Clustering". In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (pp. 353-360). IEEE.
  • Hartmann, S., Alshami, J., Ma, H., & Steinmetz, D. (2020) "A Conceptual Framework for Dynamic Planning of Alternative Routes in Road Networks". In Unknown Book (Vol. 12400 LNCS, pp. 539-554). doi:10.1007/978-3-030-62522-1_40
  • Fournier-Viger, P., He, G., Lin, J. C. W., & Gomes, H. M. (2020) "Mining attribute evolution rules in dynamic attributed graphs". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12393 LNCS (pp. 167-182). doi:10.1007/978-3-030-59065-9_14
  • Escott, K. R., Ma, H., & Chen, G. (2020) "Genetic programming based hyper heuristic approach for dynamic workflow scheduling in the cloud". In Unknown Book (Vol. 12392 LNCS, pp. 76-90). doi:10.1007/978-3-030-59051-2_6
  • Chen, G. (2020) "A new framework for multi-agent reinforcement learning - centralized training and exploration with decentralized execution via policy distillation". In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 2020-May (pp. 1801-1803).
  • Cerqueira, V., Gomes, H. M., & Bifet, A. (2020) "Unsupervised Concept Drift Detection Using a Student–Teacher Approach". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12323 LNAI (pp. 190-204). doi:10.1007/978-3-030-61527-7_13
  • Bi, Y., Xue, B., & Zhang, M. (2020) "Evolving deep forest with automatic feature extraction for image classification using genetic programming". In Unknown Book (Vol. 12269 LNCS, pp. 3-18). doi:10.1007/978-3-030-58112-1_1
  • Bahri, M., Bifet, A., Maniu, S., & Gomes, H. M. (2020) "Survey on feature transformation techniques for data streams". In IJCAI International Joint Conference on Artificial Intelligence Vol. 2021-January (pp. 4796-4802).
  • Azari, S., Xue, B., Zhang, M., & Peng, L. (2020) "A Decomposition Based Multi-objective Genetic Programming Algorithm for Classification of Highly Imbalanced Tandem Mass Spectrometry". In Unknown Book (Vol. 12047 LNCS, pp. 449-463). doi:10.1007/978-3-030-41299-9_35
  • Ardeh, M. A., Mei, Y., & Zhang, M. (2020) "A Parametric Framework for Genetic Programming with Transfer Learning for Uncertain Capacitated Arc Routing Problem". In Unknown Book (Vol. 12576 LNAI, pp. 150-162). doi:10.1007/978-3-030-64984-5_12
  • Al-Shaboti, M., Chen, G., & Welch, I. (2020) "Achieving IoT Devices Secure Sharing in Multi-User Smart Space". In 2020 IEEE 45th Conference on Local Computer Networks (LCN) (pp. 88-99). IEEE.
  • Al-Sahaf, H., Al-Sahaf, A., Xue, B., & Zhang, M. (2020) "Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances". Evolutionary Computation (Journal, MIT Press), PP, 1-34. doi:10.1162/evco_a_00284
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020) "Hessian Complexity Measure for Genetic Programming-Based Imputation Predictor Selection in Symbolic Regression with Incomplete Data". In Unknown Book (Vol. 12101 LNCS, pp. 1-17). doi:10.1007/978-3-030-44094-7_1
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020) "Genetic Programming-Based Simultaneous Feature Selection and Imputation for Symbolic Regression with Incomplete Data". In Unknown Book (Vol. 12047 LNCS, pp. 566-579). doi:10.1007/978-3-030-41299-9_44
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2020) "Genetic Programming-Based Selection of Imputation Methods in Symbolic Regression with Missing Values". In Unknown Book (Vol. 12576 LNAI, pp. 163-175). doi:10.1007/978-3-030-64984-5_13
  • Ajmal, A., Al-Sahaf, H., & Hollitt, C. (2020) "Salient motion features for visual atention models". In Proceedings of the 35th Conference on Image Vision and Computing New Zealand (IVCNZ 2020) (pp. 1-6). IEEE. doi:10.1109/IVCNZ51579.2020.9290712
  • Ain, Q. U., Al-Sahaf, H., Xue, B., & Zhang, M. (2020) "Generating Knowledge-guided Discriminative Features Using Genetic Programming for Melanoma Detection". IEEE Transactions on Emerging Topics in Computational Intelligence, xx, 1-16. doi:10.1109/TETCI.2020.2983426
  • Ain, Q. U., Al-Sahaf, H., Xue, B., & Zhang, M. (2020) "A Genetic Programming Approach to Feature Construction for Ensemble Learning in Skin Cancer Detection". In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020) (pp. 1186–1194). ACM. doi:10.1145/3377930.3390228
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., & Li, J. (2020) "Ontology-Guided Data Augmentation for Medical Document Classification". In Unknown Book (Vol. 12299 LNAI, pp. 78-88). doi:10.1007/978-3-030-59137-3_8
  • Abbasi, M. S., Al-Sahaf, H., & Welch, I. (2020) "Particle Swarm Optimization: A Wrapper-Based Feature Selection Method for Ransomware Detection and Classification". In Unknown Book (pp. 181-196).
  • Zhang, Y., Mei, Y., Zhang, B., & Jiang, K. (2019) "Divide-and-Conquer Large Scale Capacitated Arc Routing Problems with Route Cutting Off Decomposition". __. doi:10.48550/arxiv.1912.12667
  • Wang, S., Mei, Y., Park, J., & Zhang, M. (2019) "Evolving Ensembles of Routing Policies using Genetic Programming for Uncertain Capacitated Arc Routing Problem". In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 1628-1635). doi:10.1109/SSCI44817.2019.9002749
  • Wang, S., Mei, Y., Park, J., & Zhang, M. (2019) "A Two-Stage Genetic Programming Hyper-Heuristic for Uncertain Capacitated Arc Routing Problem". In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 1606-1613). doi:10.1109/SSCI44817.2019.9002912
  • Pei, W., Xue, B., Zhang, M., & Shang, L. (2019) "A Cost-sensitive Genetic Programming Approach for High-dimensional Unbalanced Classification". In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 1770-1777). doi:10.1109/SSCI44817.2019.9003041
  • O'Neill, D., Xue, B., & Zhang, M. (2019) "The Evolution of Adjacency Matrices for Sparsity of Connection in DenseNets". In International Conference Image and Vision Computing New Zealand Vol. 2019-December. doi:10.1109/IVCNZ48456.2019.8961027
  • Le Nguyen, M. H., Gomes, H. M., & Bifet, A. (2019) "Semi-supervised Learning over Streaming Data using MOA". In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 553-562). doi:10.1109/BigData47090.2019.9006217
  • Kho, Y. H., Watterson, C., Andreae, P., & Nekooei, M. (2019) "An analysis of students' writing: The design of an online repository as a writing support". In TALE 2019 - 2019 IEEE International Conference on Engineering, Technology and Education. doi:10.1109/TALE48000.2019.9225963
  • Karunakaran, D., Mei, Y., & Zhang, M. (2019) "Multitasking Genetic Programming for Stochastic Team Orienteering Problem with Time Windows". In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 1598-1605). doi:10.1109/SSCI44817.2019.9002804
  • Gomes, H. M., Mello, R. F. D., Pfahringer, B., & Bifet, A. (2019) "Feature Scoring using Tree-Based Ensembles for Evolving Data Streams". In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 761-769). doi:10.1109/BigData47090.2019.9006366
  • Desai, J., Nguyen, B. H., & Xue, B. (2019) "Multi-Label Feature Selection Using Particle Swarm Optimization: Novel Local Search Mechanisms". In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 1762-1769). doi:10.1109/SSCI44817.2019.9002734
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2019) "A Genetic Programming-based Wrapper Imputation Method for Symbolic Regression with Incomplete Data". In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 2395-2402). doi:10.1109/SSCI44817.2019.9002861
  • Ceschin, F., Botacin, M., Gomes, H. M., Oliveira, L. S., & Grégio, A. (2019) "Shallow Security". In Proceedings of the 3rd Reversing and Offensive-oriented Trends Symposium (pp. 1-9). ACM. doi:10.1145/3375894.3375898
  • Gomes, H. M., Read, J., Bifet, A., Barddal, J. P., & Gama, J. (2019) "Machine learning for streaming data". ACM SIGKDD Explorations Newsletter, 21(2), 6-22. doi:10.1145/3373464.3373470
  • MacLachlan, J., Mei, Y., Branke, J., & Zhang, M. (2019) "Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems". __. doi:10.48550/arxiv.1911.08650
  • Mirghasemi, S., Andreae, P., & Zhang, M. (2019) "Domain-independent severely noisy image segmentation via adaptive wavelet shrinkage using particle swarm optimization and fuzzy C-means". Expert Systems with Applications, 133, 126-150. doi:10.1016/j.eswa.2019.04.050
  • Gomes, H. M., Read, J., & Bifet, A. (2019) "Streaming random patches for evolving data stream classification". In Proceedings - IEEE International Conference on Data Mining, ICDM Vol. 2019-November (pp. 240-249). doi:10.1109/ICDM.2019.00034
  • Lensen, A., Xue, B., & Zhang, M. (2019) "Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis". __. doi:10.48550/arxiv.1910.10264
  • Chen, G. (2019) "A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation". arXiv.
  • Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfahringer, B., . . . Abdessalem, T. (2019) "Correction to: Adaptive random forests for evolving data stream classification (Machine Learning, (2017), 106, 9-10, (1469-1495), 10". 1007/s10994-017-5642-8). Machine Learning, 108(10), 1877-1878. doi:10.1007/s10994-019-05793-3
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2019) "A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization". IEEE Transactions on Evolutionary Computation, 23(5), 748-761. doi:10.1109/TEVC.2018.2882166
  • Ma, J., Xue, B., & Zhang, M. (2019) "A hybrid filter-wrapper feature selection approach for authorship attribution". International Journal of Innovative Computing, Information and Control, 15(5), 1989-2006. doi:10.24507/ijicic.15.05.1989
  • Jiao, R., Zeng, S., & Li, C. (2019) "A feasible-ratio control technique for constrained optimization". Information Sciences, 502, 201-217. doi:10.1016/j.ins.2019.06.030
  • Granatyr, J., Gomes, H. M., DIas, J. M., Paiva, A. M., Nunes, M. A. S. N., Scalabrin, E. E., & Spak, F. (2019) "Inferring trust using personality aspects extracted from texts". In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics Vol. 2019-October (pp. 3840-3846). doi:10.1109/SMC.2019.8914641
  • Evans, B. P., Al-Sahaf, H., Xue, B., & Zhang, M. (2019) "Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image Classification". __. doi:10.48550/arxiv.1909.13030
  • Xue, Y., Xue, B., & Zl, M. (2019) "Self-Adaptive particle swarm optimization for large-scale feature selection in classification". ACM Transactions on Knowledge Discovery from Data, 13(5). doi:10.1145/3340848
  • Tran, B., Xue, B., & Zhang, M. (2019) "Genetic programming for multiple-feature construction on high-dimensional classification". Pattern Recognition, 93, 404-417. doi:10.1016/j.patcog.2019.05.006
  • Santana, R., Marti, L., & Zhang, M. (2019) "GP-based methods for domain adaptation: using brain decoding across subjects as a test-case". Genetic Programming and Evolvable Machines, 20(3), 385-411. doi:10.1007/s10710-019-09352-6
  • Azari, S., Xue, B., Zhang, M., & Peng, L. (2019) "Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches". __. doi:10.48550/arxiv.1908.08010
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2019) "A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification". IEEE Transactions on Neural Networks and Learning Systems, 30(8), 2295-2309. doi:10.1109/TNNLS.2018.2881143
  • Chen, Q., Zhang, M., & Xue, B. (2019) "Structural Risk Minimization-Driven Genetic Programming for Enhancing Generalization in Symbolic Regression". IEEE Transactions on Evolutionary Computation, 23(4), 703-717. doi:10.1109/TEVC.2018.2881392
  • Wang, B., Xue, B., & Zhang, M. (2019) "Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks". __. doi:10.48550/arxiv.1907.12659
  • Azari, S., Xue, B., Zhang, M., & Peng, L. (2019) "Preprocessing Tandem Mass Spectra Using Genetic Programming for Peptide Identification". Journal of the American Society for Mass Spectrometry, 30(7), 1294-1307. doi:10.1007/s13361-019-02196-5
  • Zhang, M., & Cagnoni, S. (2019) "Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition". In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 1226-1251). doi:10.1145/3319619.3323388
  • Wang, C., Ma, H., Chen, G., & Hartmann, S. (2019) "A memetic NSGA-II with EDA-based local search for fully automated multiobjective web service composition". In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 421-422). doi:10.1145/3319619.3321937
  • Wang, B., Sun, Y., Xue, B., & Zhang, M. (2019) "Evolving deep neural networks by multi-objective particle swarm optimization for image classification". In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 490-498). doi:10.1145/3321707.3321735
  • Trung Nguyen, B., Browne, W. N., & Zhang, M. (2019) "Improvement of code fragment fitness to guide feature construction in XCS". In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 428-436). doi:10.1145/3321707.3321751
  • Tran, B., Xue, B., & Zhang, M. (2019) "Adaptive multi-subswarm optimisation for feature selection on high-dimensional classification". In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 481-489). doi:10.1145/3321707.3321713
  • Sadeghiram, S., Ma, H., & Chen, G. (2019) "A memetic algorithm with distance-guided crossover: Distributed data-intensive web service composition". In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 155-156). doi:10.1145/3319619.3322015
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2019) "Reuse of program trees in genetic programming with a new fitness function in high-dimensional unbalanced classification". In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 187-188). doi:10.1145/3319619.3321958
  • Nguyen, B. H., Xue, B., Zhang, M., & Andreae, P. (2019) "Population-based ensemble classifier induction for domain adaptation". In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 437-445). doi:10.1145/3321707.3321716
  • Liu, Y., Browne, W. N., & Xue, B. (2019) "Absumption to complement subsumption in learning classifier systems". In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 410-418). doi:10.1145/3321707.3321719
  • Evans, B. P., Xue, B., & Zhang, M. (2019) "What's inside the black-box? A genetic programming method for interpreting complex machine learning models". In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 1012-1020). doi:10.1145/3321707.3321726
  • Chen, Q., Xue, B., & Zhang, M. (2019) "Differential evolution for instance based transfer learning in genetic programming for symbolic regression". In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 161-162). doi:10.1145/3319619.3321941
  • Bi, Y., Xue, B., & Zhang, M. (2019) "An automated ensemble learning framework using genetic programming for image classification". In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 365-373). doi:10.1145/3321707.3321750
  • Escott, K. R., & Noble, J. (2019) "Design patterns for Angular Hotdraw". In ACM International Conference Proceeding Series. doi:10.1145/3361149.3361185
  • Zhang, F., Mei, Y., & Zhang, M. (2019) "A Two-Stage Genetic Programming Hyper-heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 347-355). IEEE. doi:10.1145/3321707.3321790
  • Wang, S., Mei, Y., & Zhang, M. (2019) "Novel Ensemble Genetic Programming Hyper-Heuristics for Uncertain Capacitated Arc Routing Problem". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 1093-1101). IEEE. doi:10.1145/3321707.3321797
  • Tan, B., Ma, H., & Mei, Y. (2019) "Novel genetic algorithm with dual chromosome representation for resource allocation in container-based clouds". In IEEE International Conference on Cloud Computing, CLOUD Vol. 2019-July (pp. 452-456). doi:10.1109/CLOUD.2019.00078
  • Shi, T., Ma, H., & Chen, G. (2019) "A genetic-based approach to location-aware cloud service brokering in multi-cloud environment". In Proceedings - 2019 IEEE International Conference on Services Computing, SCC 2019 - Part of the 2019 IEEE World Congress on Services (pp. 146-153). doi:10.1109/SCC.2019.00034
  • Liang, Y., Zhang, M., & Browne, W. N. (2019) "Figure-ground image segmentation using feature-based multi-objective genetic programming techniques". Neural Computing and Applications, 31(7), 3075-3094. doi:10.1007/s00521-017-3253-8
  • Boiko Ferreira, L. E., Murilo Gomes, H., Bifet, A., & Oliveira, L. S. (2019) "Adaptive Random Forests with Resampling for Imbalanced data Streams". In Proceedings of the International Joint Conference on Neural Networks Vol. 2019-July. doi:10.1109/IJCNN.2019.8852027
  • Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019) "Boosting decision stumps for dynamic feature selection on data streams". Information Systems, 83, 13-29. doi:10.1016/j.is.2019.02.003
  • Ardeh, M. A., Mei, Y., & Zhang, M. (2019) "Genetic Programming Hyper-Heuristic with Knowledge Transfer for Uncertain Capacitated Arc Routing Problem". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO) (pp. 334-335). IEEE. doi:10.1145/3319619.3321988
  • Wang, C., Ma, H., Chen, G., & Hartmann, S. (2019) "Memetic EDA-Based Approaches to Comprehensive Quality-Aware Automated Semantic Web Service Composition". __. doi:10.48550/arxiv.1906.07900
  • Zhang, F., Mei, Y., & Zhang, M. (2019) "Evolving Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling via Genetic Programming Hyper-heuristics". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 1366-1373). doi:10.1109/CEC.2019.8790112
  • Zhang, F., Mei, Y., & Zhang, M. (2019) "Can Stochastic Dispatching Rules Evolved by Genetic Programming Hyper-heuristics Help in Dynamic Flexible Job Shop Scheduling?". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 41-48). doi:10.1109/CEC.2019.8790030
  • Yu, Y., Feng, Y., Ma, H., Chen, A., & Wang, C. (2019) "Achieving Flexible Scheduling of Heterogeneous Workflows in Cloud through a Genetic Programming Based Approach". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 3102-3109). doi:10.1109/CEC.2019.8789896
  • Watts, T., Xue, B., & Zhang, M. (2019) "Blocky Net: A New NeuroEvolution Method". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 586-593). doi:10.1109/CEC.2019.8790302
  • Wang, C., Ma, H., Chen, G., & Hartmann, S. (2019) "Evolutionary Multitasking for Semantic Web Service Composition". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2490-2497). doi:10.1109/CEC.2019.8790085
  • Viswambaran, R. A., Chen, G., Xue, B., & Nekooei, M. (2019) "Evolutionary Design of Recurrent Neural Network Architecture for Human Activity Recognition". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 554-561). doi:10.1109/CEC.2019.8790050
  • Tran, B., Xue, B., & Zhang, M. (2019) "Variable-Length Particle Swarm Optimization for Feature Selection on High-Dimensional Classification". IEEE Transactions on Evolutionary Computation, 23(3), 473-487. doi:10.1109/TEVC.2018.2869405
  • Tan, B., Ma, H., & Mei, Y. (2019) "A Hybrid Genetic Programming Hyper-Heuristic Approach for Online Two-level Resource Allocation in Container-based Clouds". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2681-2688). doi:10.1109/CEC.2019.8790220
  • Shi, T., Ma, H., & Chen, G. (2019) "A Seeding-based GA for Location-Aware Workflow Deployment in Multi-cloud Environment". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 3364-3371). doi:10.1109/CEC.2019.8790110
  • Shi, S., Chen, Y., Yao, X., & Zhang, M. (2019) "Lightweight Evolution Strategies for Nanoswimmers-oriented in Vivo Computation". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 866-872). doi:10.1109/CEC.2019.8790356
  • Sadeghiram, S., Ma, H., & Chen, G. (2019) "Composing Distributed Data-intensive Web Services Using a Flexible Memetic Algorithm". 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2832-2839. doi:10.1109/CEC.2019.8790132
  • Ribeiro, R., Casanova, D., Teixeira, M., Wirth, A., Gomes, H. M., Borges, A. P., & Enembreck, F. (2019) "Generating action plans for poultry management using artificial neural networks". Computers and Electronics in Agriculture, 161, 131-140. doi:10.1016/j.compag.2018.02.017
  • Raymond, C., Chen, Q., Xue, B., & Zhang, M. (2019) "Genetic Programming with Rademacher Complexity for Symbolic Regression". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2657-2664). doi:10.1109/CEC.2019.8790341
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2019) "New Fitness Functions in Genetic Programming for Classification with High-dimensional Unbalanced Data". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2779-2786). doi:10.1109/CEC.2019.8789974
  • Pearson, W., Tran, C. T., Zhang, M., & Xue, B. (2019) "Multi-Round Random Subspace Feature Selection for Incomplete Gene Expression Data". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2544-2551). doi:10.1109/CEC.2019.8790237
  • Nguyen, T. B., Browne, W. N., & Zhang, M. (2019) "Online Feature-Generation of Code Fragments for XCS to Guide Feature Construction". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 3308-3315). doi:10.1109/CEC.2019.8789950
  • Ma, H., Da Silva, A. S., & Kuang, W. (2019) "NSGA-II with Local Search for Multi-objective Application Deployment in Multi-Cloud". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2800-2807). doi:10.1109/CEC.2019.8790006
  • Karunakaran, D., Mei, Y., Chen, G., & Zhang, M. (2019) "Active Sampling for Dynamic Job Shop Scheduling using Genetic Programming". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 434-441). doi:10.1109/CEC.2019.8789923
  • Jiao, R., Zeng, S., Li, C., & Pedrycz, W. (2019) "Evolutionary Constrained Multi-objective Optimization using NSGA-II with Dynamic Constraint Handling". In 2019 IEEE Congress on Evolutionary Computation (CEC) (pp. 1634-1641). IEEE. doi:10.1109/cec.2019.8790172
  • Irwin-Harris, W., Sun, Y., Xue, B., & Zhang, M. (2019) "A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 546-553). doi:10.1109/CEC.2019.8790093
  • Fu, W., Zhang, M., & Johnston, M. (2019) "Bayesian genetic programming for edge detection". Soft Computing, 23(12), 4097-4112. doi:10.1007/s00500-018-3059-3
  • Fu, W., Xue, B., Gao, X., & Zhang, M. (2019) "Genetic Programming based Transfer Learning for Document Classification with Self-taught and Ensemble Learning". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2260-2267). doi:10.1109/CEC.2019.8790318
  • Chen, Q., Xue, B., & Zhang, M. (2019) "Instance based Transfer Learning for Genetic Programming for Symbolic Regression". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 3006-3013). doi:10.1109/CEC.2019.8790217
  • Chen, Q., Xue, B., & Zhang, M. (2019) "Improving Generalization of Genetic Programming for Symbolic Regression with Angle-Driven Geometric Semantic Operators". IEEE Transactions on Evolutionary Computation, 23(3), 488-502. doi:10.1109/TEVC.2018.2869621
  • Bi, Y., Xue, B., & Zhang, M. (2019) "An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 3197-3204). doi:10.1109/CEC.2019.8790151
  • Azari, S., Zhang, M., Xue, B., & Peng, L. (2019) "Learning to Rank Peptide-Spectrum Matches Using Genetic Programming". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 3244-3251). doi:10.1109/CEC.2019.8790049
  • Ardeh, M. A., Mei, Y., & Zhang, M. (2019) "Transfer Learning in Genetic Programming Hyper-heuristic for Solving Uncertain Capacitated Arc Routing Problem". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 49-56). doi:10.1109/CEC.2019.8789920
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., & Li, J. (2019) "An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation". In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 119-126). doi:10.1109/CEC.2019.8790259
  • Huang, V., Chen, G., Fu, Q., & Wen, E. (2019) "Optimizing controller placement for software-defined networks". In 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019 (pp. 224-232).
  • Huang, V., Chen, G., & Fu, Q. (2019) "Effective Scheduling Function Design in SDN Through Deep Reinforcement Learning". In IEEE International Conference on Communications Vol. 2019-May. doi:10.1109/ICC.2019.8761938
  • Zhang, F., Mei, Y., & Zhang, M. (2019) "A New Representation in Genetic Programming for Evolving Dispatching Rules for Dynamic Flexible Job Shop Scheduling". In Proceedings of the European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP) (pp. 33-49). Springer. doi:10.1007/978-3-030-16711-0_3
  • Liu, Y., Mei, Y., Zhang, M., & Zhang, Z. (2019) "A Predictive-Reactive Approach with Genetic Programming and Cooperative Co-evolution for Uncertain Capacitated Arc Routing Problem". Evolutionary Computation. doi:10.1162/evco_a_00256
  • Wang, B., Sun, Y., Xue, B., & Zhang, M. (2019) "Evolving Deep Neural Networks by Multi-objective Particle Swarm Optimization for Image Classification". __. doi:10.48550/arxiv.1904.09035
  • Liu, X., Shi, X., Lu, G., Deng, X., Zhou, H., Yan, Z., . . . Xue, B. (2019) "The synergistic lubricating mechanism of Sn-Ag-Cu and C60 on the worn surface of M50 self-lubricating material at elevated loads". Journal of Alloys and Compounds, 777, 271-284. doi:10.1016/j.jallcom.2018.11.021
  • Wang, B., Sun, Y., Xue, B., & Zhang, M. (2019) "A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks". __. doi:10.48550/arxiv.1903.03893
  • Lin, Y., Andreae, P., Li, Z., Cai, J., & Li, H. (2019) "Real-time co-registered photoacoustic and ultrasonic imaging for early endometrial cancer detection driven by cylindrical diffuser". Journal of Innovative Optical Health Sciences, 12(2). doi:10.1142/S1793545819500020
  • Huang, V., Chen, G., Fu, Q., & Wen, E. (2019) "Optimizing Controller Placement for Software-Defined Networks". __. doi:10.48550/arxiv.1902.09451
  • Chen, G., & Peng, Y. (2019) "Off-Policy Actor-Critic in an Ensemble: Achieving Maximum General Entropy and Effective Environment Exploration in Deep Reinforcement Learning". arXiv.
  • Lensen, A., Xue, B., & Zhang, M. (2019) "Can Genetic Programming Do Manifold Learning Too?". __. doi:10.48550/arxiv.1902.02949
  • Azari, S., Xue, B., Zhang, M., & Peng, L. (2019) "GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry using Genetic Algorithm". __. doi:10.48550/arxiv.1902.00845
  • Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019) "Merit-guided dynamic feature selection filter for data streams". Expert Systems with Applications, 116, 227-242. doi:10.1016/j.eswa.2018.09.031
  • Sadeghiram, S., Ma, H., & Chen, G. (2019) "Composing Distributed Data-intensive Web Services Using a Flexible Memetic Algorithm". __. doi:10.48550/arxiv.1901.09894
  • Sadeghiram, S., Hui, M. A., & Chen, G. (2019) "Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition". Retrieved from http://arxiv.org/abs/1901.
  • Sadeghiram, S., MA, H., & Chen, G. (2019) "Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition". __. doi:10.48550/arxiv.1901.05564
  • Xue, B., Xu, Z., Liu, Y., & Ma, W. (2019) "Enhanced Mechanical and Tribological Properties of Graphene-Reinforced TiAl Matrix Composites". Tribology Transactions, 62(1), 117-126. doi:10.1080/10402004.2018.1523513
  • Zeng, S., Jiao, R., Li, C., & Wang, R. (2019) "Constrained optimisation by solving equivalent dynamic loosely-constrained multiobjective optimisation problem". International Journal of Bio-Inspired Computation, 13(2), 86. doi:10.1504/ijbic.2019.098406
  • Wang, C., Ma, H., Chen, G., & Hartmann, S. (2019) "Towards Robust Web Service Composition with Stochastic Service Failures Based on a Genetic Algorithm". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11919 LNAI (pp. 445-459). doi:10.1007/978-3-030-35288-2_36
  • Wang, C., Ma, H., & Chen, G. (2019) "Using EDA-Based Local Search to Improve the Performance of NSGA-II for Multiobjective Semantic Web Service Composition". In Unknown Book (Vol. 11707 LNCS, pp. 434-451). doi:10.1007/978-3-030-27618-8_32
  • Wang, B., Sun, Y., Xue, B., & Zhang, M. (2019) "A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks". In Unknown Book (Vol. 11672 LNAI, pp. 650-663). doi:10.1007/978-3-030-29894-4_52
  • Tariq, H., Al-Sahaf, H., & Welch, I. (2019) "Modelling and prediction of resource utilization of hadoop clusters: A machine learning approach". In Proceedings of the 12th IEEE/ACM international conference on utility and cloud computing (pp. 93-100).
  • Steinmetz, D., Merz, F., Ma, H., & Hartmann, S. (2019) "A graph model for taxi ride sharing supported by graph databases". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11788 LNCS (pp. 108-116). doi:10.1007/978-3-030-33223-5_10
  • Sadeghiram, S., Ma, H., & Chen, G. (2019) "Composing Distributed Data-Intensive Web Services Using Distance-Guided Memetic Algorithm". In Unknown Book (Vol. 11707 LNCS, pp. 411-422). doi:10.1007/978-3-030-27618-8_30
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2019) "Genetic programming for job shop scheduling". In Studies in Computational Intelligence (Vol. 779, pp. 143-167). doi:10.1007/978-3-319-91341-4_8
  • Muller, B., Al-Sahaf, H., Xue, B., & Zhang, M. (2019) "Transfer Learning: A Building Block Selection in Genetic Programming for Symbolic Regression". In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO 2019) (pp. 350-351). ACM. doi:10.1145/3319619.3322072
  • Masood, A., Chen, G., Mei, Y., Al-Sahaf, H., & Zhang, M. (2019) "Genetic Programming with Pareto Local Search for Many-Objective Job Shop Scheduling". In Unknown Conference Vol. 11919 (pp. 536-548). Springer. doi:10.1007/978-3-030-35288-2_43
  • Madukwe, K. J., & Gao, X. (2019) "The Thin Line Between Hate and Profanity". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11919 LNAI (pp. 344-356). doi:10.1007/978-3-030-35288-2_28
  • Maclachlan, J., Mei, Y., Branke, J., & Zhang, M. (2019) "Genetic programming hyper-heuristics with vehicle collaboration for uncertain capacitated arc routing problems". Evolutionary Computation, 28(4), 563-593. doi:10.1162/evco_a_00267
  • Liu, X., Shi, X., Lu, G., Zhou, H., Deng, X., Yan, Z., . . . Xue, B. (2019) "Effect of different microporous parameters on mechanical and frictional properties of M50 self-lubricating materials: Simulation analysis and experimental study". Materials Research Express, 6(5). doi:10.1088/2053-1591/ab00b3
  • Lensen, A., Xue, B., & Zhang, M. (2019) "Genetic programming for evolving similarity functions for clustering: Representations and analysis". Evolutionary Computation, 28(4), 531-561. doi:10.1162/evco_a_00264
  • Lensen, A., Xue, B., & Zhang, M. (2019) "Can Genetic Programming Do Manifold Learning Too?". In GENETIC PROGRAMMING, EUROGP 2019 Vol. 11451 (pp. 114-130). doi:10.1007/978-3-030-16670-0_8
  • Lensen, A., Xue, B., & Zhang, M. (2019) "Can Genetic Programming Do Manifold Learning Too?". In Unknown Book (Vol. 11451 LNCS, pp. 114-130). doi:10.1007/978-3-030-16670-0_8
  • Lensen, A. (2019) "Evolutionary Feature Manipulation in Unsupervised Learning". (PhD Thesis).
  • Jiao, R., Zeng, S., Li, C., Jiang, Y., & Jin, Y. (2019) "A complete expected improvement criterion for Gaussian process assisted highly constrained expensive optimization". Information Sciences, 471, 80-96. doi:10.1016/j.ins.2018.09.003
  • Iqbal, M., Al-Sahaf, H., Xue, B., & Zhang, M. (2019) "Genetic Programming with Transfer Learning for Texture Image Classification". Soft Computing, 23, 12859-12871. doi:10.1007/s00500-019-03843-5
  • Gomes, H. M., Bifet, A., Fournier-Viger, P., Granatyr, J., & Read, J. (2019) "Network of experts: Learning from evolving data streams through network-based ensembles". In Unknown Book (Vol. 11953 LNCS, pp. 704-716). doi:10.1007/978-3-030-36708-4_58
  • Desai, J., Nguyen, B. H., & Xue, B. (2019) "Multi-label Feature Selection Using Particle Swarm Optimization: Novel Initialization Mechanisms". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11919 LNAI (pp. 510-522). doi:10.1007/978-3-030-35288-2_41
  • Deng, X., Shi, X., Liu, X., Liu, Y., & Xue, B. (2019) "Study on the thickness of lubricating film of M50-Ag self-lubricating composites". Lubrication Science, 31(1-2), 11-20. doi:10.1002/ls.1442
  • Barddal, J. P., Gomes, H. M., & Enembreck, F. (2019) "On Social Network-Based Algorithms for Data Stream Clustering". In Studies in Big Data (Vol. 41, pp. 297-317). doi:10.1007/978-3-319-89803-2_13
  • Azari, S., Xue, B., Zhang, M., & Peng, L. (2019) "Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-Based Scoring Function for Re-ranking Peptide-Spectrum Matches". In Unknown Book (Vol. 11672 LNAI, pp. 474-487). doi:10.1007/978-3-030-29894-4_38
  • Azari, S., Xue, B., Zhang, M., & Peng, L. (2019) "GA-novo: De novo peptide sequencing via tandem mass spectrometry using genetic algorithm". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11454 LNCS (pp. 72-89). doi:10.1007/978-3-030-16692-2_6
  • Ansari Ardeh, M., Mei, Y., & Zhang, M. (2019) "A novel genetic programming algorithm with knowledge transfer for uncertain capacitated arc routing problem". In Unknown Book (Vol. 11670 LNAI, pp. 196-200). doi:10.1007/978-3-030-29908-8_16
  • Al-Shaboti, M., Welch, I., & Chen, A. (2019) "Iot application-centric access control (acac)". In Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security (pp. 685-687).
  • Al-Shaboti, M., Chen, A., & Welch, I. (2019) "Automatic device selection and access policy generation based on user preference for iot activity workflow". In 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 769-774). IEEE.
  • Al-Sahaf, H., & Welch, I. (2019) "A genetic programming approach to feature selection and construction for ransomware, phishing and spam detection". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 332-333).
  • Al-Sahaf, H., Bi, Y., Chen, Q., Lensen, A., Mei, Y., Sun, Y., . . . Zhang, M. (2019) "A survey on evolutionary machine learning". Journal of the Royal Society of New Zealand (TNZR), 49, 205-228. doi:10.1080/03036758.2019.1609052
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2019) "Genetic Programming for Imputation Predictor Selection and Ranking in Symbolic Regression with High-Dimensional Incomplete Data". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11919 LNAI (pp. 523-535). doi:10.1007/978-3-030-35288-2_42
  • Ain, Q. U., Xue, B., Al-Sahaf, H., & Zhang, M. (2019) "Multi-tree Genetic Programming with a New Fitness Function for Melanoma Detection". In Proceedings of 2019 IEEE Congress on Evolutionary Computation (CEC 2019) (pp. 880-887). IEEE. doi:10.1109/CEC.2019.8790282
  • Ain, Q. U., Xue, B., Al-Sahaf, H., & Zhang, M. (2019) "Genetic Programming for Multiple Feature Construction in Skin Cancer Image Classification". In Proceedings of the 34th International Conference on Image and Vision Computing New Zealand (IVCNZ 2019) (pp. 1-6). IEEE. doi:10.1109/IVCNZ48456.2019.8961001
  • Afzali, S., Al-Sahaf, H., Xue, B., Hollitt, C., & Zhang, M. (2019) "Genetic Programming for Feature Selection and Feature Combination in Salient Object Detection". In Unknown Conference Vol. 11454 (pp. 308-324). Springer. doi:10.1007/978-3-030-16692-2_21
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., & Li, J. (2019) "Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11672 LNAI (pp. 457-473). doi:10.1007/978-3-030-29894-4_37
  • Tran, C. T., Zhang, M., Andreae, P., Xue, B., & Bui, L. T. (2018) "Improving performance of classification on incomplete data using feature selection and clustering". Applied Soft Computing Journal, 73, 848-861. doi:10.1016/j.asoc.2018.09.026
  • Liu, X., Shi, X., Huang, Y., Deng, X., Lu, G., Yan, Z., & Xue, B. (2018) "Tribological behavior and self-healing functionality of M50 material covered with surface micropores filled with Sn-Ag-Cu". Tribology International, 128, 365-375. doi:10.1016/j.triboint.2018.07.050
  • Nguyen, S., Zhang, M., Alahakoon, D., & Tan, K. C. (2018) "Visualizing the evolution of computer programs for genetic programming [Research Frontier]". IEEE Computational Intelligence Magazine, 13(4), 77-94. doi:10.1109/MCI.2018.2866731
  • Fu, W., Xu, B., Zhang, M., & Johnston, M. (2018) "Fast unsupervised edge detection using genetic programming [Application notes]". IEEE Computational Intelligence Magazine, 13(4), 46-58. doi:10.1109/MCI.2018.2866729
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2018) "Automatically Evolving CNN Architectures Based on Blocks". __. doi:10.48550/arxiv.1810.11875
  • Liu, X., Shi, X., Huang, Y., Deng, X., Yan, Z., & Xue, B. (2018) "Anti-friction and wear properties of the friction surface of M50-10 wt%(50Sn40Ag10Cu) composite". Journal of Alloys and Compounds, 765, 7-17. doi:10.1016/j.jallcom.2018.06.160
  • Zheng, Y., Peng, H., Zhang, X., Gao, X., & Li, J. (2018) "Predicting Drug Targets from Heterogeneous Spaces using Anchor Graph Hashing and Ensemble Learning". In Proceedings of the International Joint Conference on Neural Networks Vol. 2018-July. doi:10.1109/IJCNN.2018.8489028
  • Fletcher, S., Verma, B., Jan, Z. M., & Zhang, M. (2018) "The Optimized Selection of Base-Classifiers for Ensemble Classification using a Multi-Objective Genetic Algorithm". In Proceedings of the International Joint Conference on Neural Networks Vol. 2018-July. doi:10.1109/IJCNN.2018.8489467
  • Ferreira, L. E. B., Barddal, J. P., Enembreck, F., & Gomes, H. M. (2018) "An Experimental Perspective on Sampling Methods for Imbalanced Learning from Financial Databases". In Proceedings of the International Joint Conference on Neural Networks Vol. 2018-July. doi:10.1109/IJCNN.2018.8489290
  • Chen, G., Peng, Y., & Zhang, M. (2018) "Constrained Expectation-Maximization Methods for Effective Reinforcement Learning". In Proceedings of the International Joint Conference on Neural Networks Vol. 2018-July. doi:10.1109/IJCNN.2018.8488990
  • Jiao, R., Sun, Y., Sun, J., Jiang, Y., & Zeng, S. (2018) "Antenna design using dynamic multi‐objective evolutionary algorithm". IET Microwaves, Antennas & Propagation, 12(13), 2065-2072. doi:10.1049/iet-map.2018.5298
  • Asafuddoula, M., Verma, B., & Zhang, M. (2018) "A Divide-and-Conquer-Based Ensemble Classifier Learning by Means of Many-Objective Optimization". IEEE Transactions on Evolutionary Computation, 22(5), 762-777. doi:10.1109/TEVC.2017.2782826
  • Wang, B., Sun, Y., Xue, B., & Zhang, M. (2018) "Evolving Deep Convolutional Neural Networks by Variable-Length Particle Swarm Optimization for Image Classification". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477735
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2018) "An Experimental Study on Hyper-parameter Optimization for Stacked Auto-Encoders". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477921
  • Shi, T., Ma, H., & Chen, G. (2018) "Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477708
  • Sadeghiram, S., Ma, H., & Chen, G. (2018) "Cluster-Guided Genetic Algorithm for Distributed Data-intensive Web Service Composition". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477729
  • O'Neill, D., Lensen, A., Xue, B., & Zhang, M. (2018) "Particle Swarm Optimisation for Feature Selection and Weighting in High-Dimensional Clustering". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477974
  • Cheng, X., Browne, W. N., & Zhang, M. (2018) "Decomposition Based Multi-Objective Evolutionary Algorithm in XCS for Multi-Objective Reinforcement Learning". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477931
  • Bi, Y., Zhang, M., & Xue, B. (2018) "Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477911
  • Bai, X., Gao, X., & Xue, B. (2018) "Particle Swarm Optimization Based Two-Stage Feature Selection in Text Mining". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477773
  • Azari, S., Zhang, M., Xue, B., & Peng, L. (2018) "Genetic Programming for Preprocessing Tandem Mass Spectra to Improve the Reliability of Peptide Identification". In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. doi:10.1109/CEC.2018.8477810
  • Chen, G., Peng, Y., & Zhang, M. (2018) "Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization". arXiv.
  • Chen, G., Peng, Y., & Zhang, M. (2018) "Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization". __. doi:10.48550/arxiv.1809.00403
  • Nguyen, H. B., Xue, B., & Andreae, P. (2018) "PSO with surrogate models for feature selection: static and dynamic clustering-based methods". Memetic Computing, 10(3), 291-300. doi:10.1007/s12293-018-0254-9
  • Chen, M., Xu, Z., Xue, B., Liu, Y., & Ma, W. (2018) "Friction and wear performance of a NiAl-8 wt% serpentine-2 wt%TiC composite at high temperatures". Materials Research Express, 5(9). doi:10.1088/2053-1591/aad85b
  • Wang, B., Sun, Y., Xue, B., & Zhang, M. (2018) "A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification". __. doi:10.48550/arxiv.1808.06661
  • Tran, C. T., Zhang, M., Andreae, P., Xue, B., & Bui, L. T. (2018) "An effective and efficient approach to classification with incomplete data". Knowledge-Based Systems, 154, 1-16. doi:10.1016/j.knosys.2018.05.013
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2018) "Automatically designing CNN architectures using genetic algorithm for image classification". __. doi:10.48550/arxiv.1808.03818
  • Xue, B., Ma, W., & Liu, Y. (2018) "Friction and Wear Behavior of TiAl Matrix Composites Incorporated with Silver and Molybdenum Disulfide". Journal of Materials Engineering and Performance, 27(8), 4176-4182. doi:10.1007/s11665-018-3533-1
  • Liu, X., Shi, X., Huang, Y., Deng, X., Yan, Z., & Xue, B. (2018) "The Sliding Wear and Frictional Behavior of M50-10 wt". %(Sn-Ag-Cu) Self-Lubricating Materials at Elevated Temperatures. Journal of Materials Engineering and Performance, 27(8), 4291-4299. doi:10.1007/s11665-018-3484-6
  • Zhang, M., & Cagnoni, S. (2018) "Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition". In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 1221-1257). doi:10.1145/3205651.3207859
  • Xue, B., & Zhang, M. (2018) "Evolutionary computation for feature selection and feature construction". In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 1198-1220). doi:10.1145/3205651.3207862
  • Wang, C., Ma, H., & Chen, G. (2018) "EDA-based approach to comprehensive ality-aware automated semantic web service composition". In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 147-148). doi:10.1145/3205651.3205734
  • Jiao, R., Zeng, S., Li, C., & Jiang, Y. (2018) "Dynamic constrained multi-objective evolutionary algorithms with a novel selection strategy for constrained optimization". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 213-214). ACM. doi:10.1145/3205651.3205653
  • Peng, Y., Chen, G., Singh, H., & Zhang, M. (2018) "NEAT for large-scale reinforcement learning through evolutionary feature learning and policy gradient search". In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 490-497). doi:10.1145/3205455.3205536
  • Nguyen, S., Zhang, M., & Tan, K. C. (2018) "Adaptive charting genetic programming for dynamic flexible job shop scheduling". In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 1159-1166). doi:10.1145/3205455.3205531
  • Nguyen, B. H., Xue, B., & Andreae, P. (2018) "A particle swarm optimization based feature selection approach to transfer learning in classification". In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 37-44). doi:10.1145/3205455.3205540
  • Lensen, A., Xue, B., & Zhang, M. (2018) "Automatically evolving difficult benchmark feature selection datasets with genetic programming". In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 458-465). doi:10.1145/3205455.3205552
  • Jiao, R., Zeng, S., Li, C., Jiang, Y., & Wang, J. (2018) "Expected improvement of constraint violation for expensive constrained optimization". In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1039-1046). ACM. doi:10.1145/3205455.3205458
  • Yska, D., Mei, Y., & Zhang, M. (2018) "Feature Construction in Genetic Programming Hyper-Heuristic for Dynamic Flexible Job Shop Scheduling". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO) (pp. 149-150). ACM. doi:10.1145/3205651.3205741
  • Mei, Y., & Zhang, M. (2018) "Genetic Programming Hyper-Heuristic for Multi-Vehicle Uncertain Capacitated Arc Routing Problem". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO) (pp. 141-142). ACM. doi:10.1145/3205651.3205661
  • Liu, Y., Yan, Z., Shi, X., Huang, Y., Xue, B., & Ibrahim, A. M. M. (2018) "Differences in tribological performance between spark plasma sintering and laser melting deposition for fabrication of Ni3Al matrix self-lubricating composites". Materials Research Express, 5(7). doi:10.1088/2053-1591/aaceef
  • Xue, B., Liu, X., Shi, X., Huang, Y., Lu, G., & Wu, C. (2018) "Effect of graphene nanoplatelets on tribological properties of titanium alloy matrix composites at varying sliding velocities". Materials Research Express, 5(6). doi:10.1088/2053-1591/aac703
  • Nguyen, S., Mei, Y., & Zhang, M. (2018) "Guest editorial: special issue on automated design and adaptation of heuristics for scheduling and combinatorial optimisation". Genetic Programming and Evolvable Machines, 19(1-2), 5-7. doi:10.1007/s10710-017-9317-9
  • Mei, Y., & Zhang, M. (2018) "Genetic Programming Hyper-Heuristic for Stochastic Team Orienteering Problem with Time Windows". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. doi:10.1109/CEC.2018.8477983
  • Liu, Y., Deng, X., Shi, X., Liu, X., & Xue, B. (2018) "Effect of synthesis methods on tribological performance of M50-Ag-Ti3SiC2 self-lubricating composites". Materials Research Express, 5(6). doi:10.1088/2053-1591/aacc84
  • Burmester, G., Ma, H., Steinmetz, D., & Hartmannn, S. (2018) "Big Data and Data Analytics in Aviation". In Advances in Aeronautical Informatics: Technologies Towards Flight 4.0 (pp. 55-65). doi:10.1007/978-3-319-75058-3_5
  • Chen, G., Peng, Y., & Zhang, M. (2018) "An Adaptive Clipping Approach for Proximal Policy Optimization". arXiv.
  • Chen, G., Peng, Y., & Zhang, M. (2018) "An Adaptive Clipping Approach for Proximal Policy Optimization". __. doi:10.48550/arxiv.1804.06461
  • Han, Y., Yang, K., Jing, P., Xue, B., & Ma, W. (2018) "Mechanical and tribological properties of NiAl/muscovite composites". Journal of Alloys and Compounds, 741, 765-774. doi:10.1016/j.jallcom.2018.01.169
  • Yska, D., Mei, Y., & Zhang, M. (2018) "Genetic Programming Hyper-Heuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling". In Proceedings of the European Conference on Genetic Programming (EuroGP) (pp. 306-321). Springer. doi:10.1007/978-3-319-77553-1_19
  • Rahman, I. M. H., Hollitt, C., & Zhang, M. (2018) "Feature Map Quality Score Estimation Through Regression". IEEE Transactions on Image Processing, 27(4), 1793-1808. doi:10.1109/TIP.2017.2785623
  • Park, J., Mei, Y., Nguyen, S., Chen, G., & Zhang, M. (2018) "Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling". In Proceedings of the European Conference on Genetic Programming (EuroGP) (pp. 253-270). Springer. doi:10.1007/978-3-319-77553-1_16
  • Masood, A., Chen, G., Mei, Y., & Zhang, M. (2018) "Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling". In Proceedings of the European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP) (pp. 116-131). Springer. doi:10.1007/978-3-319-77449-7_8
  • da Silva, A. S., Ma, H., Mei, Y., & Zhang, M. (2018) "A Hybrid Memetic Approach for Fully Automated Multi-Objective Web Service Composition". In Proceedings of the IEEE International Conference on Web Services (ICWS) (pp. 26-33). IEEE. doi:10.1109/ICWS.2018.00011
  • Gomes, H. M., Barddal, J. P., Enembreck, A. F., & Bifet, A. (2018) "A survey on ensemble learning for data stream classification". ACM Computing Surveys, 50(2). doi:10.1145/3054925
  • Wang, B., Sun, Y., Xue, B., & Zhang, M. (2018) "Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification". __. doi:10.48550/arxiv.1803.06492
  • Liu, X., Shi, X., Huang, Y., Deng, X., Lu, G., Yan, Z., . . . Xue, B. (2018) "The Sliding Wear and Friction Behavior of M50-Graphene Self-Lubricating Composites Prepared by Laser Additive Manufacturing at Elevated Temperature". Journal of Materials Engineering and Performance, 27(3), 985-996. doi:10.1007/s11665-018-3187-z
  • Lensen, A., Xue, B., & Zhang, M. (2018) "Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming". __. doi:10.48550/arxiv.1802.00554
  • Cheng, H., Yao, X., Yang, S., & Zhang, M. (2018) "Guest Editorial: Special Issue on Computational Intelligence for Cloud Computing". IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 1-2. doi:10.1109/TETCI.2017.2788548
  • Hancer, E., Xue, B., & Zhang, M. (2018) "Differential evolution for filter feature selection based on information theory and feature ranking". Knowledge-Based Systems, 140, 103-119. doi:10.1016/j.knosys.2017.10.028
  • Zhang, F., Mei, Y., & Zhang, M. (2018) "Surrogate-assisted genetic programming for dynamic flexible job shop scheduling". In Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI) (pp. 766-772). Springer. doi:10.1007/978-3-030-03991-2_69
  • Zhang, F., Mei, Y., & Zhang, M. (2018) "Genetic programming with multi-tree representation for dynamic flexible job shop scheduling". In Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI) (pp. 472-484). Springer. doi:10.1007/978-3-030-03991-2_43
  • Xie, J., Mei, Y., Ernst, A. T., Li, X., & Song, A. (2018) "A bi-level optimization model for grouping constrained storage location assignment problems". IEEE Transactions on Cybernetics, 48, 385-398. doi:10.1109/TCYB.2016.2638820
  • Wang, C., Ma, H., Chen, G., & Hartmann, S. (2018) "Towards fully automated semantic web service composition based on estimation of distribution algorithm". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 458-471). doi:10.1007/978-3-030-03991-2_42
  • Wang, C., Ma, H., Chen, A., & Hartmann, S. (2018) "Knowledge-driven automated web service composition—An EDA-based approach". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11234 LNCS (pp. 135-150). doi:10.1007/978-3-030-02925-8_10
  • Wang, B., Sun, Y., Xue, B., & Zhang, M. (2018) "A hybrid differential evolution approach to designing deep convolutional neural networks for image classification". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 237-250). doi:10.1007/978-3-030-03991-2_24
  • Tran, C. T., Zhang, M., Xue, B., & Andreae, P. (2018) "Genetic programming with interval functions and ensemble learning for classification with incomplete data". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 577-589). doi:10.1007/978-3-030-03991-2_53
  • Tariq, H., Welch, I., & Al-Sahaf, H. (2018) "An Investigation of Hadoop Parameters in SDN-enabled Clusters". In 2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (pp. 1-9). IEEE.
  • Tan, B., Ma, H., & Mei, Y. (2018) "A Genetic Programming Hyper-heuristic Approach for Online Resource Allocation in Container-Based Clouds". In Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI) (pp. 146-152). Springer. doi:10.1007/978-3-030-03991-2_15
  • Tallón-Ballesteros, A. J., Tuba, M., Xue, B., & Hashimoto, T. (2018) "Feature selection and interpretable feature transformation: A preliminary study on feature engineering for classification algorithms". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11315 LNCS (pp. 280-287). doi:10.1007/978-3-030-03496-2_31
  • Tallón-Ballesteros, A. J., Correia, L., & Xue, B. (2018) "Featuring the attributes in supervised machine learning". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10870 LNAI (pp. 350-362). doi:10.1007/978-3-319-92639-1_29
  • Steinmetz, D., Dyballa, D., Ma, H., & Hartmann, S. (2018) "Using a conceptual model to transform road networks from openstreetmap to a graph database". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11157 LNCS (pp. 301-315). doi:10.1007/978-3-030-00847-5_22
  • Sim, K., Kaufmann, P., Ascheid, G., Bacardit, J., Cagnoni, S., Cotta, C., . . . Zhang, M. (2018) "Preface (Vol". 10784 LNCS).
  • Shi, T., Ma, H., & Chen, G. (2018) "Multi-objective container consolidation in cloud data centers". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 783-795). doi:10.1007/978-3-030-03991-2_71
  • Pei, W., Xue, B., Shang, L., & Zhang, M. (2018) "Genetic programming based on granular computing for classification with high-dimensional data". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 643-655). doi:10.1007/978-3-030-03991-2_58
  • Park, J., Mei, Y., Nguyen, S., Chen, G., & Zhang, M. (2018) "Evolutionary Multitask Optimisation for Dynamic Job Shop Scheduling Using Niched Genetic Programming". In Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI) (pp. 739-751). Springer. doi:10.1007/978-3-030-03991-2_66
  • Park, J., Mei, Y., Nguyen, S., Chen, G., & Zhang, M. (2018) "An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling". Applied Soft Computing, 63, 72-86. doi:10.1016/j.asoc.2017.11.020
  • Pappa, G. L., Emmerich, M. T. M., Bazzan, A., Browne, W., Deb, K., Doerr, C., . . . Zhang, M. (2018) "Tutorials at PPSN 2018". In Lecture Notes in Computer Science (pp. 477-489). Springer International Publishing. doi:10.1007/978-3-319-99259-4_38
  • O’Neill, D., Xue, B., & Zhang, M. (2018) "Co-evolution of novel tree-like ANNs and activation functions: An observational study". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 616-629). doi:10.1007/978-3-030-03991-2_56
  • Mitrovic, T., Xue, B., & Li, X. (2018) "Preface (Vol". 11320 LNAI).
  • Mi, Z., Shang, L., & Xue, B. (2018) "Multi-dimensional optical flow embedded genetic programming for anomaly detection in crowded scenes". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11302 LNCS (pp. 486-497). doi:10.1007/978-3-030-04179-3_43
  • Masood, A., Chen, G., Mei, Y., & Zhang, M. (2018) "Adaptive Reference Point Generation for Many-Objective Optimization Using NSGA-III". In Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI) (pp. 358-370). Springer. doi:10.1007/978-3-030-03991-2_34
  • Mahmood, M. A., Welch, I., & Andreae, P. (2018) "Enhanced event reliability in wireless sensor networks". In 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA) (pp. 93-100). IEEE.
  • MacLachlan, J., Mei, Y., Branke, J., & Zhang, M. (2018) "An Improved Genetic Programming Hyper-Heuristic for the Uncertain Capacitated Arc Routing Problem". In Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI) (pp. 432-444). Springer. doi:10.1007/978-3-030-03991-2_40
  • Liu, Y., Browne, W. N., & Xue, B. (2018) "Hierarchical learning classifier systems for polymorphism in heterogeneous niches". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 397-409). doi:10.1007/978-3-030-03991-2_37
  • Liu, Y., Browne, W. N., & Xue, B. (2018) "Adapting Bagging and Boosting to Learning Classifier Systems". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10784 LNCS (pp. 405-420). doi:10.1007/978-3-319-77538-8_28
  • Lensen, A., Xue, B., & Zhang, M. (2018) "Generating redundant features with unsupervised multi-tree genetic programming". In Unknown Book (Vol. 10781 LNCS, pp. 84-100). doi:10.1007/978-3-319-77553-1_6
  • Koay, A., Chen, A., Welch, I., & Seah, W. K. G. (2018) "A new multi classifier system using entropy-based features in DDoS attack detection". In 2018 International Conference on Information Networking (ICOIN) (pp. 162-167). IEEE.
  • Karunakaran, D., Mei, Y., Chen, G., & Zhang, M. (2018) "Sampling Heuristics for Multi-objective Dynamic Job Shop Scheduling Using Island Based Parallel Genetic Programming". In Proceedings of the International Conference on Parallel Problem Solving from Nature (PPSN) (pp. 347-359). Springer. doi:10.1007/978-3-319-99259-4_28
  • Hartmann, S., & Ma, H. (2018) "Preface (Vol". 11029 LNCS).
  • Hartmann, S., & Ma, H. (2018) "Preface (Vol". 11030 LNCS).
  • Hartmann, S., & Ma, H. (2018) "Preface (Vol". 11250 LNCS). doi:10.1063/1.4828676
  • Hancer, E., Xue, B., Zhang, M., Karaboga, D., & Akay, B. (2018) "Pareto front feature selection based on artificial bee colony optimization". Information Sciences, 422, 462-479. doi:10.1016/j.ins.2017.09.028
  • Gomes, H. M., Barddal, J. P., Boiko, L. E., & Bifet, A. (2018) "Adaptive random forests for data stream regression". In ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 267-272).
  • Evans, B., Al-Sahaf, H., Xue, B., & Zhang, M. (2018) "Evolutionary Deep Learning: A Genetic Programming Approach to Image Classification". In Proceedings of 2018 IEEE Congress on Evolutionary Computation (CEC 2018) (pp. 1538-1545). IEEE. doi:10.1109/CEC.2018.8477933
  • da Silva, A. S., Mei, Y., Ma, H., & Zhang, M. (2018) "Evolutionary computation for automatic Web service composition: an indirect representation approach". Journal of Heuristics, 24, 425-456. doi:10.1007/s10732-017-9330-4
  • Chen, K., Zhou, F., & Xue, B. (2018) "Particle swarm optimization for feature selection with adaptive mechanism and new updating strategy". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 419-431). doi:10.1007/978-3-030-03991-2_39
  • Chen, G., Peng, Y., & Zhang, M. (2018) "Constrained Expectation-Maximization Methods for Effective Reinforcement Learning". In 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (pp. 171-178). Retrieved from https://www.webofscience.
  • Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., & García-Sánchez, P. (2018) "Preface (Vol". 10781 LNCS).
  • Bi, Y., Xue, B., & Zhang, M. (2018) "An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming". In Unknown Book (Vol. 10784 LNCS, pp. 421-438). doi:10.1007/978-3-319-77538-8_29
  • Bi, Y., Xue, B., & Zhang, M. (2018) "A gaussian filter-based feature learning approach using genetic programming to image classification". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 251-257). doi:10.1007/978-3-030-03991-2_25
  • Al-Shaboti, M., Welch, I., Chen, A., & Mahmood, M. A. (2018) "Towards secure smart home IoT: Manufacturer and user network access control framework". In 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA) (pp. 892-899). IEEE.
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2018) "A hybrid GP-KNN imputation for symbolic regression with missing values". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11320 LNAI (pp. 345-357). doi:10.1007/978-3-030-03991-2_33
  • Ajmal, A., Hollitt, C., Frean, M., & Al-Sahaf, H. (2018) "A Comparison of RGB and HSV Colour Spaces for Visual Attention Models". In Proceedings of the 33rd International Conference on Image and Vision Computing New Zealand (IVCNZ 2018) (pp. 1-6). IEEE. doi:10.1109/IVCNZ.2018.8634752
  • Ain, Q. U., Xue, B., Al-Sahaf, H., & Zhang, M. (2018) "Genetic Programming for Feature Selection and Feature Construction in Skin Cancer Image Classification". In Unknown Conference Vol. 11012 (pp. 732-745). Springer. doi:10.1007/978-3-319-97304-3_56
  • Ain, Q. U., Al-Sahaf, H., Xue, B., & Zhang, M. (2018) "A Multi-tree Genetic Programming Representation For Melanoma Detection Using Local and Global Features". In Unknown Conference Vol. 11320 (pp. 111-123). Springer. doi:10.1007/978-3-030-03991-2_12
  • Afzali, S., Al-Sahaf, H., Xue, B., Hollitt, C., & Zhang, M. (2018) "Foreground and Background Feature Fusion using a Convex Hull based Center Prior for Salient Object Detection". In Proceedings of the 33rd International Conference on Image and Vision Computing New Zealand (IVCNZ 2018) (pp. 1-6). IEEE. doi:10.1109/IVCNZ.2018.8634726
  • Afzali, S., Al-Sahaf, H., Xue, B., Hollitt, C., & Zhang, M. (2018) "A Genetic Programming approach for Constructing Foreground and Background Saliency Features for Salient Object Detection". In Unknown Conference Vol. 11320 (pp. 209-215). Springer. doi:10.1007/978-3-030-03991-2_21
  • Abdollahi, M., Gao, X., Mei, Y., Ghosh, S., & Li, J. (2018) "Uncovering Discriminative Knowledge-Guided Medical Concepts for Classifying Coronary Artery Disease Notes". In Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI) (pp. 104-110). Springer. doi:10.1007/978-3-030-03991-2_11
  • Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., & García-Sánchez, P. (Eds.) (2018) "Genetic Programming". __. doi:10.1007/978-3-319-77553-1
  • Tran, C. T., Zhang, M., Andreae, P., Xue, B., & Bui, L. T. (2017) "An ensemble of rule-based classifiers for incomplete data". In Proceedings - 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems, IES 2017 Vol. 2017-January (pp. 7-12). doi:10.1109/IESYS.2017.8233553
  • Chen, Q. (2017) "Improving the Generalisation of Genetic Programming for Symbolic Regression". (PhD Thesis).
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2017) "A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification". __. doi:10.48550/arxiv.1712.05042
  • Xue, B., Jing, P., & Ma, W. (2017) "Tribological Properties of NiAl Matrix Composites Filled with Serpentine Powders". Journal of Materials Engineering and Performance, 26(12), 5816-5824. doi:10.1007/s11665-017-3058-z
  • Omidvar, M. N., Yang, M., Mei, Y., Li, X., & Yao, X. (2017) "DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization". IEEE Transactions on Evolutionary Computation, 21(6), 929-942. doi:10.1109/tevc.2017.2694221
  • Jiao, R., Zeng, S., Alkasassbeh, J. S., & Li, C. (2017) "Dynamic multi-objective evolutionary algorithms for single-objective optimization". Applied Soft Computing, 61, 793-805. doi:10.1016/j.asoc.2017.08.030
  • Huang, Y., Xue, B., Shi, X., Yang, K., Zhai, W., & Xiao, Y. (2017) "Study on Tribological Performance of NiAl Matrix Self-Lubricating Composites Containing Graphene at Different Loads". Tribology Transactions, 60(6), 1043-1052. doi:10.1080/10402004.2016.1245455
  • Zhang, M. (2017) "Keynote talks: Evolutionary feature selection and dimensionality reduction". In 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES) (pp. ix-xii). IEEE. doi:10.1109/iesys.2017.8233551
  • Sun, Y., Xue, B., & Zhang, M. (2017) "Evolving Deep Convolutional Neural Networks for Image Classification". (pp. 14 pages). Retrieved from http://arxiv.org/abs/1710.
  • Sun, Y., Xue, B., Zhang, M., & Yen, G. G. (2017) "Evolving Deep Convolutional Neural Networks for Image Classification". __. doi:10.48550/arxiv.1710.10741
  • Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfharinger, B., . . . Abdessalem, T. (2017) "Adaptive random forests for evolving data stream classification". Machine Learning, 106(9-10), 1469-1495. doi:10.1007/s10994-017-5642-8
  • Chen, Q., Zhang, M., & Xue, B. (2017) "Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression". IEEE Transactions on Evolutionary Computation, 21(5), 792-806. doi:10.1109/TEVC.2017.2683489
  • Xue, B. (2017) "Sebastian Ventura and Jose Maria Luna: Pattern mining with evolutionary algorithms". Genetic Programming and Evolvable Machines, 18(3), 407-409. doi:10.1007/s10710-017-9306-z
  • Liu, X., Shi, X., Wu, C., Yang, K., Huang, Y., Deng, X., . . . Xue, B. (2017) "Tribological behavior of M50-MoS2 self-lubricating composites from 150 to 450 °C". Materials Chemistry and Physics, 198, 145-153. doi:10.1016/j.matchemphys.2017.05.044
  • Luo, W., Hart, E., & Zhang, M. (2017) "Guest Editorial: Special Issue on Emergent Topics in Artificial Immune Systems". IEEE Transactions on Emerging Topics in Computational Intelligence, 1(4), 235. doi:10.1109/TETCI.2017.2723738
  • Qu, Y., Ng, B., Yu, H., Andreae, P., & Seah, W. K. G. (2017) "Fitness evaluation for channel assignment algorithms in IEEE 802". 11 WMNs. In 2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017 (pp. 361-364). doi:10.1109/CCNC.2017.7983135
  • Nourmohammadzadeh, A., Hartmann, S., & Ma, H. (2017) "A parallel hybrid GA-PSO approach with dynamic rule-based parameter setting". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 215-216). ACM. doi:10.1145/3067695.3076051
  • Liang, Y., Zhang, M., & Browne, W. N. (2017) "Learning figure-ground image segmentors by genetic programming". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 239-240). ACM. doi:10.1145/3067695.3075989
  • Phillips, T., Zhang, M., & Xue, B. (2017) "Genetic programming for solving common and domain-independent generic recursive problems". In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 1279-1286). doi:10.1109/CEC.2017.7969452
  • Nguyen, S., & Zhang, M. (2017) "A PSO-based hyper-heuristic for evolving dispatching rules in job shop scheduling". In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 882-889). doi:10.1109/CEC.2017.7969402
  • Nguyen, H. B., Xue, B., Andreae, P., & Zhang, M. (2017) "Particle Swarm Optimisation with genetic operators for feature selection". In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 286-293). doi:10.1109/CEC.2017.7969325
  • Boiko Ferreira, L. E., Barddal, J. P., Gomes, H. M., & Enembreck, F. (2017) "Improving credit risk prediction in online peer-To-peer (P2P) lending using imbalanced learning techniques". In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI Vol. 2017-November (pp. 175-181). doi:10.1109/ICTAI.2017.00037
  • Bi, Y., Zhang, M., & Xue, B. (2017) "An automatic region detection and processing approach in genetic programming for binary image classification". In International Conference Image and Vision Computing New Zealand Vol. 2017-December (pp. 1-6). doi:10.1109/IVCNZ.2017.8402469
  • Xue, Y., Jiang, J., Xue, B., & Zhang, M. (2017) "A classification method based on self-adaptive artificial bee colony". In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings Vol. 2018-January (pp. 1-8). doi:10.1109/SSCI.2017.8285232
  • Wang, C., Xue, B., & Shang, L. (2017) "PSO-based parameters selection for the bilateral filter in image denoising". In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 51-58). doi:10.1145/3071178.3071231
  • Tran, C. T., Zhang, M., Andreae, P., & Xue, B. (2017) "Multiple imputation and genetic programming for classification with incomplete data". In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 521-528). Online. doi:10.1145/3071178.3071181
  • Tran, C. T., Zhang, M., Andreae, P., & Xue, B. (2017) "Genetic programming based feature construction for classification with incomplete data". In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 1033-1040). doi:10.1145/3071178.3071183
  • Nekooei, S. M., Chen, G., & Rayudu, R. K. (2017) "Automatic design of fuzzy logic controllers for medium access control in wireless body area networks – An evolutionary approach". Applied Soft Computing, 56, 245-261. doi:10.1016/j.asoc.2017.02.022
  • Mikula, M., Gao, X., & Machova, K. (2017) "Adapting sentiment analysis system from English to Slovak". In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings Vol. 2018-January (pp. 1-8). doi:10.1109/SSCI.2017.8285313
  • Lensen, A., Xue, B., & Zhang, M. (2017) "GPGC: Genetic programming for automatic clustering using a flexible non-hyper-spherical graph-based approach". In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 449-456). doi:10.1145/3071178.3071222
  • Hancer, E., Xue, B., & Zhang, M. (2017) "A differential evolution based feature selection approach using an improved filter criterion". In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings Vol. 2018-January (pp. 1-8). doi:10.1109/SSCI.2017.8285300
  • Ghifary, M., Balduzzi, D., Kleijn, W. B., & Zhang, M. (2017) "Scatter component analysis: A unified framework for domain adaptation and domain generalization". IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(7), 1414-1430. doi:10.1109/TPAMI.2016.2599532
  • Asafuddoula, M., Verma, B., & Zhang, M. (2017) "An incremental ensemble classifier learning by means of a rule-based accuracy and diversity comparison". In Proceedings of the International Joint Conference on Neural Networks Vol. 2017-May (pp. 1924-1931). doi:10.1109/IJCNN.2017.7966086
  • Tran, B., Xue, B., & Zhang, M. (2017) "A New Representation in PSO for Discretization-Based Feature Selection". IEEE Transactions on Cybernetics, 14 pages. doi:10.1109/TCYB.2017.2714145
  • Xue, B., Zhu, H., & Shi, X. (2017) "Tribological performance of NiAl alloy containing graphene nanoplatelets under different velocities". Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 231(6), 799-809. doi:10.1177/1350650116680426
  • Liang, Y., Zhang, M., & Browne, W. N. (2017) "Image feature selection using genetic programming for figure-ground segmentation". Engineering Applications of Artificial Intelligence, 62, 96-108. doi:10.1016/j.engappai.2017.03.009
  • Iqbal, M., Browne, W. N., & Zhang, M. (2017) "Extending XCS with cyclic graphs for scalability on complex Boolean problems". Evolutionary Computation, 25(2), 173-204. doi:10.1162/EVCO_a_00167
  • Xue, B., & Zhang, M. (2017) "Evolutionary feature manipulation in data mining/big data". ACM SIGEVOlution, 10(1), 4-11. doi:10.1145/3089251.3089252
  • Barddal, J. P., Gomes, H. M., Enembreck, F., & Pfahringer, B. (2017) "A survey on feature drift adaptation: Definition, benchmark, challenges and future directions". Journal of Systems and Software, 127, 278-294. doi:10.1016/j.jss.2016.07.005
  • Xue, B., Lane, M. C., Liu, I., & Zhang, M. (2017) "Dimension reduction in classification using particle swarm optimisation and statistical variable grouping information". In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. doi:10.1109/SSCI.2016.7850126
  • Lensen, A., Xue, B., & Zhang, M. (2017) "Particle swarm optimisation representations for simultaneous clustering and feature selection". In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. doi:10.1109/SSCI.2016.7850124
  • Liang, Y., Zhang, M., & Browne, W. N. (2017) "Genetic programming for evolving figure-ground segmentors from multiple features". Applied Soft Computing, 51, 83-95. doi:10.1016/j.asoc.2016.07.055
  • Rahman, I. M. H., Hollitt, C., & Zhang, M. (2017) "A dynamic feature map integration approach for predicting human fixation". In International Conference Image and Vision Computing New Zealand Vol. 0 (pp. 6 pages). doi:10.1109/IVCNZ.2016.7804420
  • Zhang, Y., Mei, Y., Tang, K., & Jiang, K. (2017) "Memetic algorithm with route decomposing for periodic capacitated arc routing problem". Applied Soft Computing, 52, 1130-1142. doi:10.1016/j.asoc.2016.09.017
  • Zeng, S., Jiao, R., Li, C., Li, X., & Alkasassbeh, J. S. (2017) "A General Framework of Dynamic Constrained Multiobjective Evolutionary Algorithms for Constrained Optimization". IEEE Transactions on Cybernetics, 1-11. doi:10.1109/tcyb.2017.2647742
  • Xue, Y., Jiang, J., Xue, B., & Zhang, M. (2017) "A Classification Method based on Self-adaptive Artificial Bee Colony". In 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) (pp. 1038-1045). Honolulu, HI: IEEE. Retrieved from http://gateway.webofknowledge.
  • Wang, Chen., Ma, Hui., Chen, G., & Hartmann, Sven. (2017) "GP-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition". In The 11th International Conference on Simulated Evolution and Learning.
  • Wang, C., Ma, H., Chen, A., & Hartmann, S. (2017) "Comprehensive quality-aware automated semantic web service composition". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10400 LNAI (pp. 195-207). doi:10.1007/978-3-319-63004-5_16
  • Tran, C. T., Zhang, M., Andreae, P., Xue, B., & Bui, L. T. (2017) "Multiple Imputation and Ensemble Learning for Classification with Incomplete Data". In G. Leu (Ed.), Proceedings in Adaptation, Learning and Optimization 8 (pp. 401-415). Canberra, Australia. doi:10.1007/978-3-319-49049-6_29
  • Tran, C. T., Zhang, M., Andreae, P., & Xue, B. (2017) "Bagging and Feature Selection for Classification with Incomplete Data". In G. Squillero, & K. Sim (Eds.), Unknown Conference (pp. 471-486). Springer International Publishing. doi:10.1007/978-3-319-55849-3_31
  • Tran, B., Xue, B., & Zhang, M. (2017) "Using Feature Clustering for GP-Based Feature Construction on High-Dimensional Data". In Proceedings of 20th European Conference on Genetic Programming (pp. 210-226). Amsterdam, Netherlands: Springer International Publishing.
  • Tran, B., Xue, B., & Zhang, M. (2017) "Class Dependent Multiple Feature Construction Using Genetic Programming for High-Dimensional Data". In Proceedings of the 30th Australasian Joint Conference on Artificial Intelligence (AI) Vol. 10400 (pp. 182-194). Melbourne, VIC, Australia: Springer International Publishing.
  • Tran, B., Picek, S., & Xue, B. (2017) "Automatic Feature Construction for Network Intrusion Detection". In Proceedings of the 11th International Conference on Simulated Evolution and Learning (SEAL) Vol. 10593 (pp. 569-580). Shenzhen, China: Springer International Publishing.
  • Tan, B., Ma, H., & Mei, Y. (2017) "A NSGA-II-based approach for service resource allocation in Cloud". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 2574-2581). IEEE. doi:10.1109/CEC.2017.7969618
  • Tan, B., Huang, H., Ma, H., & Zhang, M. (2017) "Binary PSO for web service location-allocation". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10142 LNAI (pp. 366-377). doi:10.1007/978-3-319-51691-2_31
  • Squillero, G., Sim, K., Ascheid, G., Bacardit, J., Brabazon, A., Burelli, P., . . . Zhang, M. (2017) "Preface (Vol". 10199 LNCS).
  • Shi, Y., Tan, K. C., Zhang, M., Tang, K., & Li, X. (2017) "Preface (Vol". 10593 LNCS).
  • Rahaman, M. S., Mei, Y., Hamilton, M., & Salim, F. D. (2017) "CAPRA: A contour-based accessible path routing algorithm". Information Sciences, 385, 157-173. doi:10.1016/j.ins.2016.12.041
  • Peng, Yiming., Chen, G., & Zhang, Mengjie. (2017) "A Sandpile Model for Reliable Actor-Critic Reinforcement Learning". In Proceedings of the International Joint Conference on Neural Networks (pp. 4014-4021). Anchorage, AK, USA: Institute of Electrical and Electronics Engineers Inc.. doi:10.1109/IJCNN.2017.7966362
  • Peng, Y., Chen, G., Zhang, M., & Mei, Y. (2017) "Effective Policy Gradient Search for Reinforcement Learning Through NEAT Based Feature Extraction". In Proceedings of the Asia-Pacific Conference on Simulated Evolution and Learning (SEAL) (pp. 473-485). Springer. doi:10.1007/978-3-319-68759-9_39
  • Peng, Y., Chen, G., Holdaway, S., Mei, Y., & Zhang, M. (2017) "Automated state feature learning for actor-critic reinforcement learning through NEAT". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 135-136). ACM. doi:10.1145/3067695.3076035
  • Park, J., Mei, Y., Nguyen, S., Chen, G., & Zhang, M. (2017) "Investigating the generality of genetic programming based hyper-heuristic approach to dynamic job shop scheduling with machine breakdown". In Proceedings of the Australasian Conference on Artificial Life and Computational Intelligence (ACALCI) (pp. 301-313). Springer. doi:10.1007/978-3-319-51691-2_26
  • Omidvar, M. N., Yang, M., Mei, Y., Li, X., & Yao, X. (2017) "DG2: A faster and more accurate differential grouping for large-scale black-box optimization". IEEE Transactions on Evolutionary Computation, 21, 929-942. doi:10.1109/TEVC.2017.2694221
  • O’Neill, D., Xue, B., Al-Sahaf, H., & Zhang, M. (2017) "Common subtrees in related problems: A novel transfer learning approach for genetic programming". In Proceedings of 2017 IEEE Congress on Evolutionary Computation (CEC 2017) (pp. 1287-1294). IEEE. doi:10.1109/CEC.2017.7969453
  • Nguyen, S., Mei, Y., & Zhang, M. (2017) "Genetic programming for production scheduling: a survey with a unified framework". Complex and Intelligent Systems, 3, 41-66. doi:10.1007/s40747-017-0036-x
  • Nguyen, H. B., Xue, B., Ishibuchi, H., Andreae, P., & Zhang, M. (2017) "Multiple Reference Points MOEA/D for Feature Selection". In Proceedings of GECCO '17 Companion (pp. 157-158). Berlin, Germany: ACM. doi:10.1145/3067695.3075985
  • Nguyen, H. B., Xue, B., & Andreae, P. (2017) "Surrogate-model based particle swarm optimisation with local search for feature selection in classification". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10199 LNCS (pp. 487-505). Amsterdam, The Netherlands: Springer, Cham. doi:10.1007/978-3-319-55849-3_32
  • Nguyen, H. B., Xue, B., & Andreae, P. (2017) "A hybrid GA-GP method for feature reduction in classification". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10593 LNCS (pp. 591-604). doi:10.1007/978-3-319-68759-9_48
  • Nguyen, B. H., Xue, B., & Andreae, P. (2017) "A Novel Binary Particle Swarm Optimization Algorithm and Its Applications on Knapsack and Feature Selection Problems". In Unknown Conference (pp. 319-332). Springer International Publishing. doi:10.1007/978-3-319-49049-6_23
  • Nekooei, M.., Chen, G., & Rayudu, R.. (2017) "Cooperative Design of Two Level Fuzzy Logic Controllers for Medium Access Control in Wireless Body Area Networks". In The 11th International Conference on Simulated Evolution and Learning.
  • Mei, Y., Nguyen, S., & Zhang, M. (2017) "Evolving time-invariant dispatching rules in job shop scheduling with genetic programming". In Proceedings of the European Conference on Genetic Programming (EuroGP) (pp. 147-163). Springer. doi:10.1007/978-3-319-55696-3_10
  • Mei, Y., Nguyen, S., & Zhang, M. (2017) "Constrained Dimensionally Aware Genetic Programming for Evolving Interpretable Dispatching Rules in Dynamic Job Shop Scheduling". In Proceedings of the Asia-Pacific Conference on Simulated Evolution and Learning (SEAL) (pp. 435-447). Springer. doi:10.1007/978-3-319-68759-9_36
  • Mei, Y., Nguyen, S., Xue, B., & Zhang, M. (2017) "An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming". IEEE Transactions on Emerging Topics in Computational Intelligence, 1, 339-353. doi:10.1109/TETCI.2017.2743758
  • Masood, A., Mei, Y., Chen, G., & Zhang, M. (2017) "A PSO-based reference point adaption method for genetic programming hyper-heuristic in many-objective job shop scheduling". In Proceedings of the Australasian Conference on Artificial Life and Computational Intelligence (ACALCI) (pp. 326-338). Springer. doi:10.1007/978-3-319-51691-2_28
  • Liu, Y., Xue, B., & Browne, W. N. (2017) "Visualisation and optimisation of learning classifier systems for multiple domain learning". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10593 LNCS (pp. 448-461). doi:10.1007/978-3-319-68759-9_37
  • Liu, Y., Mei, Y., Zhang, M., & Zhang, Z. (2017) "Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 290-297). ACM. doi:10.1145/3071178.3071185
  • Liang, Y., Zhang, M., & Browne, W. N. (2017) "Wrapper feature construction for figure-ground image segmentation using genetic programming". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10142 LNAI (pp. 111-123). doi:10.1007/978-3-319-51691-2_10
  • Lensen, A., Xue, B., & Zhang, M. (2017) "Using particle swarm optimisation and the silhouette metric to estimate the number of clusters, select features, and perform clustering". In Unknown Book (Vol. 10199 LNCS, pp. 538-554). doi:10.1007/978-3-319-55849-3_35
  • Lensen, A., Xue, B., & Zhang, M. (2017) "New representations in genetic programming for feature construction in k-means clustering". In Unknown Book (Vol. 10593 LNCS, pp. 543-555). doi:10.1007/978-3-319-68759-9_44
  • Lensen, A., Xue, B., & Zhang, M. (2017) "Improving K-means Clustering with Genetic Programming for Feature Construction". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 237-238). ACM. doi:10.1145/3067695.3075962
  • Kumar, S., Gao, X., & Welch, I. (2017) "Cluster-than-label: Semi-supervised approach for domain adaptation". In 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA) (pp. 704-711). IEEE.
  • Karunakaran, D., Mei, Y., Chen, G., & Zhang, M. (2017) "Toward evolving dispatching rules for dynamic job shop scheduling under uncertainty". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 282-289). ACM. doi:10.1145/3071178.3071202
  • Karunakaran, D., Mei, Y., Chen, G., & Zhang, M. (2017) "Evolving dispatching rules for dynamic Job shop scheduling with uncertain processing times". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 364-371). IEEE. doi:10.1109/CEC.2017.7969335
  • Karunakaran, D., Mei, Y., Chen, G., & Zhang, M. (2017) "Dynamic job shop scheduling under uncertainty using genetic programming". Springer. doi:10.1007/978-3-319-49049-6_14
  • Jacobsen-Grocott, J., Mei, Y., Chen, G., & Zhang, M. (2017) "Evolving heuristics for Dynamic Vehicle Routing with Time Windows using genetic programming". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 1948-1955). IEEE. doi:10.1109/CEC.2017.7969539
  • Islam, M. J., Li, X., & Mei, Y. (2017) "A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO". Applied Soft Computing, 59, 182-196. doi:10.1016/j.asoc.2017.04.050
  • Iqbal, M., Xue, B., Al-Sahaf, H., & Zhang, M. (2017) "Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification". IEEE Transactions on Evolutionary Computation, 21, 569-587. doi:10.1109/TEVC.2017.2657556
  • Huo, J., Xue, B., Shang, L., & Zhang, M. (2017) "Genetic programming for multi-objective test data generation in search based software testing". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10400 LNAI (pp. 169-181). doi:10.1007/978-3-319-63004-5_14
  • Huang, Guiying., Fu, Qiang., Chen, G., Wen, Elliott., & Hart, Jonnathan. (2017) "A Bindingless Architecture for Distributed SDN Controllers". In The 42nd IEEE Conference on Local Computer Networks.
  • Hardwick-Smith, W., Peng, Y., Chen, G., Mei, Y., & Zhang, M. (2017) "Evolving Transferable Artificial Neural Networks for Gameplay Tasks via NEAT with Phased Searching". In Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI) (pp. 39-51). Springer. doi:10.1007/978-3-319-63004-5_4
  • Fu, W., Xue, B., Zhang, M., & Gao, X. (2017) "Transductive transfer learning in genetic programming for document classification". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10593 LNCS (pp. 556-568). Shenzhen, China. doi:10.1007/978-3-319-68759-9_45
  • da Silva, A. S., Moshi, E., Ma, H., & Hartmann, S. (2017) "A QoS-aware web service composition approach based on genetic programming and graph databases". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10439 LNCS (pp. 37-44). doi:10.1007/978-3-319-64471-4_4
  • da Silva, A. S., Mei, Y., Ma, H., & Zhang, M. (2017) "Fragment-based genetic programming for fully automated multi-objective web service composition". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 353-360). ACM. doi:10.1145/3071178.3071199
  • Da Silva, A. S., Ma, H., Zhang, M., & Hartmann, S. (2017) "Handling branched web service composition with a QoS-aware graph-based method". In D. Bridge, & H. Stuckenschmidt (Eds.), E-Commerce and Web Technologies 17th International Conference, EC-Web 2016 (pp. 154-169). Porto, Portugal: Springer. doi:10.1007/978-3-319-53676-7_12
  • Chen, Q., Zhang, M., & Xue, B. (2017) "New Geometric Semantic Operators in Genetic Programming: Perpendicular Crossover and Random Segment Mutation". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 223-224). ACM. doi:10.1145/3067695.3076008
  • Chen, Q., Zhang, M., & Xue, B. (2017) "New geometric semantic operators in genetic programming: perpendicular crossover and random segment mutation". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 223-224). Berlin, Germany.
  • Chen, Q., Zhang, M., & Xue, B. (2017) "Geometric semantic genetic programming with perpendicular crossover and random segment mutation for symbolic regression". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10593 LNCS (pp. 422-434). doi:10.1007/978-3-319-68759-9_35
  • Chen, Q., Zhang, M., & Xue, B. (2017) "Genetic Programming with Embedded Feature Construction for High-Dimensional Symbolic Regression". In Unknown Conference (pp. 87-102). Springer International Publishing. doi:10.1007/978-3-319-49049-6_7
  • Chen, Q., Xue, B., Mei, Y., & Zhang, M. (2017) "Geometric semantic crossover with an angle-aware mating scheme in genetic programming for symbolic regression". In Proceedings of the European Conference on Genetic Programming (EuroGP) (pp. 229-245). Springer. doi:10.1007/978-3-319-55696-3_15
  • Al-Sahaf, H., Zhang, M., Al-Sahaf, A., & Johnston, M. (2017) "Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors". IEEE Transactions on Evolutionary Computation, 21, 825-844. doi:10.1109/TEVC.2017.2685639
  • Al-Sahaf, H., Xue, B., & Zhang, M. (2017) "Evolving Texture Image Descriptors Using A Multitree Genetic Programming Representation". In Proceedings of the 2017 Genetic and Evolutionary Computation Conference (GECCO 2017) (pp. 219-220). ACM. doi:10.1145/3067695.3076039
  • Al-Sahaf, H., Xue, B., & Zhang, M. (2017) "A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors". In Unknown Conference Vol. 10593 (pp. 499-511). Springer. doi:10.1007/978-3-319-68759-9_41
  • Al-Sahaf, H., Al-Sahaf, A., Xue, B., Johnston, M., & Zhang, M. (2017) "Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming". IEEE Transactions on Evolutionary Computation, 21, 83-101. doi:10.1109/TEVC.2016.2577548
  • Al-Sahaf, H. (2017) "Genetic Programming for Automatically Synthesising Robust Image Descriptors with A Small Number of Instances". (PhD Thesis).
  • Ain, Q. U., Xue, B., Al-Sahaf, H., & Zhang, M. (2017) "Genetic programming for skin cancer detection in dermoscopic images". In Proceedings of 2017 IEEE Congress on Evolutionary Computation (CEC 2017) (pp. 2420-2427). IEEE. doi:10.1109/CEC.2017.7969598
  • Afzali, S., Xue, B., Al-Sahaf, H., & Zhang, M. (2017) "A Supervised Feature Weighting Method for Salient Object Detection using Particle Swarm Optimization". In 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) (pp. 3191-3198). Retrieved from https://www.webofscience.
  • Afzali, S., Xue, B., Al-Sahaf, H., Hollitt, C., & Zhang, M. (2017) "A Supervised Feature Weighting Method for Salient Object Detection using PSO". In Proceedings of 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017) (pp. 1-8). IEEE. doi:10.1109/SSCI.2017.8280948
  • Hartmann, S., Ma, H., & Vechsamutvaree, P. (2016) "Providing ontology-based privacy-aware data access through web services and service composition". Transactions on Large-Scale Data and Knowledge-Centred Systems, XXX Lecture Notes in Computer Science, vol 10130, 109-131. doi:10.1007/978-3-662-54054-1_5
  • Iqbal, M., Naqvi, S. S., Browne, W. N., Hollitt, C., & Zhang, M. (2016) "Learning feature fusion strategies for various image types to detect salient objects". Pattern Recognition, 60, 106-120. doi:10.1016/j.patcog.2016.05.020
  • Chen, G., Douch, C. I. J., & Zhang, M. (2016) "Accuracy-based learning classifier systems for multistep reinforcement learning: A fuzzy logic approach to handling continuous inputs and learning continuous actions". IEEE Transactions on Evolutionary Computation, 20(6), 953-971. doi:10.1109/TEVC.2016.2560139
  • Barddal, J. P., Gomes, H. M., Enembreck, F., & Barthès, J. P. (2016) "SNCStream+: Extending a high quality true anytime data stream clustering algorithm". Information Systems, 62, 60-73. doi:10.1016/j.is.2016.06.007
  • Xue, B., & Zhang, M. (2016) "Evolutionary computation for feature manipulation: Key challenges and future directions". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 3061-3067). doi:10.1109/CEC.2016.7744176
  • Tran, C. T., Zhang, M., & Andreae, P. (2016) "Directly evolving classifiers for missing data using genetic programming". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 5278-5285). doi:10.1109/CEC.2016.7748361
  • Phillips, T., Zhang, M., & Xue, B. (2016) "Genetic programming for evolving programs with recursive structures". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 5044-5051). doi:10.1109/CEC.2016.7748329
  • Nguyen, S., Zhang, M., & Tan, K. C. (2016) "Maximising total weighted number of activities for reservation with slack". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 3370-3377). doi:10.1109/CEC.2016.7744216
  • Nekooei, S. M., Chen, G., & Rayudu, R. K. (2016) "Evolutionary design of fuzzy logic controllers for medium access control in WBAN". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 2821-2828). doi:10.1109/CEC.2016.7744145
  • Liang, Y., Zhang, M., & Browne, W. N. (2016) "Figure-ground image segmentation using genetic programming and feature selection". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 3839-3846). doi:10.1109/CEC.2016.7744276
  • Koleejan, C., & Gao, X. (2016) "View-based text representation". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 263-270). Vancouver, BC, Canada. doi:10.1109/CEC.2016.7743804
  • Iqbal, M., Zhang, M., & Xue, B. (2016) "Improving classification on images by extracting and transferring knowledge in genetic programming". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 3582-3589). doi:10.1109/CEC.2016.7744243
  • Haslam, E., Xue, B., & Zhang, M. (2016) "Further investigation on genetic programming with transfer learning for symbolic regression". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 3598-3605). doi:10.1109/CEC.2016.7744245
  • Cheng, X., Chen, G., & Zhang, M. (2016) "An XCS-based algorithm for multi-objective reinforcement learning". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 4007-4014). Vancouver, BC, Canada. doi:10.1109/CEC.2016.7744298
  • Chen, Q., Xue, B., Niu, B., & Zhang, M. (2016) "Improving generalisation of genetic programming for high-dimensional symbolic regression with feature selection". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 3793-3800). doi:10.1109/CEC.2016.7744270
  • Cagnoni, S., & Zhang, M. (2016) "Evolutionary computer vision and image processing: Some FAQs, current challenges and future perspectives". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 1267-1271). doi:10.1109/CEC.2016.7743933
  • Burling-Claridge, F., Iqbal, M., & Zhang, M. (2016) "Evolutionary algorithms for classification of mammographie densities using local binary patterns and statistical features". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 3847-3854). doi:10.1109/CEC.2016.7744277
  • Alvarez, I. M., Browne, W. N., & Zhang, M. (2016) "Compaction for code fragment based learning classifier systems - Redux". In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 2217-2224). doi:10.1109/CEC.2016.7744062
  • Niu, B., Zhang, F., Li, L., & Wu, L. (2016) "Particle Swarm Optimization for Yard Truck Scheduling in Container Terminal with a Cooperative Strategy". __. doi:10.1007/978-3-319-49049-6_24
  • Huang, Y., Ibrahim, A. M. M., Shi, X., Radwan, A. R., Zhai, W., Yang, K., & Xue, B. (2016) "Tribological Characterization of NiAl Self-Lubricating Composites Containing V2O5 Nanowires". Journal of Materials Engineering and Performance, 25(11), 4941-4951. doi:10.1007/s11665-016-2339-2
  • Xue, B., Zhu, Q., Shi, X., Zhai, W., Yang, K., & Huang, Y. (2016) "Microstructure and Functional Mechanism of Friction Layer in Ni3Al Matrix Composites with Graphene Nanoplatelets". Journal of Materials Engineering and Performance, 25(10), 4126-4133. doi:10.1007/s11665-016-2264-4
  • Xue, B., & Chen, G. (2016) "Guest editorial: special issue on evolutionary optimization, feature reduction and learning". Soft Computing, 20(10), 3771-3773. doi:10.1007/s00500-016-2285-9
  • Nguyen, H. B., Xue, B., Liu, I., Andreae, P., & Zhang, M. (2016) "New mechanism for archive maintenance in PSO-based multi-objective feature selection". Soft Computing, 20(10), 3927-3946. doi:10.1007/s00500-016-2128-8
  • da Silva, A. S., Ma, H., & Zhang, M. (2016) "Genetic programming for QoS-aware web service composition and selection". Soft Computing, 20(10), 3851-3867. doi:10.1007/s00500-016-2096-z
  • Tran, C. T., Zhang, M., Andreae, P., & Xue, B. (2016) "Improving performance for classification with incomplete data using wrapper-based feature selection". Evolutionary Intelligence, 9(3), 81-94. doi:10.1007/s12065-016-0141-6
  • Nguyen, H. B., Xue, B., & Andreae, P. (2016) "Mutual information for feature selection: estimation or counting?". Evolutionary Intelligence, 9(3), 95-110. doi:10.1007/s12065-016-0143-4
  • Cagnoni, S., & Zhang, M. (2016) "Foreword: special issue on evolutionary computer vision and pattern recognition". Evolutionary Intelligence, 9(3), 53-54. doi:10.1007/s12065-016-0142-5
  • Limtrairut, P., Marshall, S., & Andreae, P. (2016) "Mobile learning application for computer science students: A transactional distance perspective". In ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 285-286). doi:10.1145/2960310.2960350
  • Li, X., Liu, J., & Zhang, F. (2016) "Different effects of provider recommendations and consumer reviews on consumers' shopping efficiency for different product types". In 2016 13th International Conference on Service Systems and Service Management, ICSSSM 2016. doi:10.1109/ICSSSM.2016.7538521
  • Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2016) "A Survey on Evolutionary Computation Approaches to Feature Selection". IEEE Transactions on Evolutionary Computation, 20(4), 606-626. doi:10.1109/TEVC.2015.2504420
  • Rahman, I., Hollitt, C., & Zhang, M. (2016) "Contextual-based top-down saliency feature weighting for target detection". Machine Vision and Applications, 27(6), 893-914. doi:10.1007/s00138-016-0754-x
  • Zhang, M., & Xue, B. (2016) "Evolutionary computation for feature selection and feature construction". In GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 861-881). doi:10.1145/2908961.2927002
  • Tran, C. T., Zhang, M., Andreae, P., & Xue, B. (2016) "Directly constructing multiple features for classification with missing data using genetic programming with interval functions". In GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 69-70). doi:10.1145/2908961.2909002
  • Sawczuk da Silva, A., Ma, H., & Zhang, M. (2016) "A graph-based QoS-aware method for web service composition with branching". In GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 131-132). doi:10.1145/2908961.2909044
  • Chen, Q., Xue, B., Shang, L., & Zhang, M. (2016) "Improving generalisation of genetic programming for symbolic regression with structural risk minimisation". In GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 709-716). doi:10.1145/2908812.2908842
  • Alvarez, I. M., Browne, W. N., & Zhang, M. (2016) "Human-inspired scaling in learning classifier systems: Case study on the N-bit multiplexer problem set". In GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 429-436). doi:10.1145/2908812.2908813
  • Zhang, F., Li, L., Liu, J., & Chu, X. (2016) "Artificial Bee Colony Optimization for Yard Truck Scheduling and Storage Allocation Problem". __. doi:10.1007/978-3-319-42294-7_81
  • Niu, B., Liu, J., Zhang, F., & Yi, W. (2016) "A Cooperative Structure-Redesigned-Based Bacterial Foraging Optimization with Guided and Stochastic Movements". __. doi:10.1007/978-3-319-42294-7_82
  • Ghifary, M., Kleijn, W. B., Zhang, M., Balduzzi, D., & Li, W. (2016) "Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation". __. doi:10.48550/arxiv.1607.03516
  • Tan, L., Wang, H., Zhang, F., & Feng, Y. (2016) "A Multiobjective Bacterial Optimization Method Based on Comprehensive Learning Strategy for Environmental/Economic Power Dispatch". __. doi:10.1007/978-3-319-41009-8_43
  • Koppen, M., & Xue, B. (2016) "Welcome message from program chairs". In Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. doi:10.1109/SOCPAR.2015.7492832
  • Carnegie, D. A., Andreae, P., Watterson, C. A., & Bubendorfer, K. (2016) "The development of postgraduate ICT programmes: For an industry that does not want traditional postgraduate students". In IEEE Global Engineering Education Conference, EDUCON Vol. 10-13-April-2016 (pp. 702-708). doi:10.1109/EDUCON.2016.7474627
  • Gomes, H. M. (2016) "Student research abstract: Advances in network-based ensemble classifiers for evolving data streams". In Proceedings of the ACM Symposium on Applied Computing Vol. 04-08-April-2016 (pp. 958-959). doi:10.1145/2851613.2852021
  • Tran, B., Xue, B., & Zhang, M. (2016) "Genetic programming for feature construction and selection in classification on high-dimensional data". Memetic Computing, 8, 3-15.
  • Fu, W., Johnston, M., & Zhang, M. (2016) "Genetic programming for edge detection: a Gaussian-based approach". Soft Computing, 20(3), 1231-1248. doi:10.1007/s00500-014-1585-1
  • Yu, Y., Ma, H., & Zhang, M. (2016) "A genetic programming approach to distributed execution of data-intensive web service compositions". In ACM International Conference Proceeding Series Vol. 01-05-February-2016. doi:10.1145/2843043.2843046
  • Branke, J., Nguyen, S., Pickardt, C. W., & Zhang, M. (2016) "Automated Design of Production Scheduling Heuristics: A Review". IEEE Transactions on Evolutionary Computation, 20(1), 110-124. doi:10.1109/TEVC.2015.2429314
  • Barddal, J. P., Gomes, H. M., & Enembreck, F. (2016) "A survey on feature drift adaptation". In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI Vol. 2016-January (pp. 1053-1060). doi:10.1109/ICTAI.2015.150
  • Zhang, Mengjie., Ma, Hui., & Tan, Boxiong. (2016) "Optimization of Location Allocation of Web Services Using a Modified Non-dominated Sorting Genetic Algorithm". Switzerland: Springer International Publishing Switzerland. Retrieved from http://link.springer.com/chapter/10.
  • Zhang, Mengjie., Chen, Gang., & Karunakaran, Deepak. (2016) "Parallel Multi-objective Job Shop Scheduling Using Genetic Programming (Vol". 9592). Switzerland: Springer, Cham. Retrieved from http://link.springer.com/chapter/10.
  • Zhang, M. (2016) "Surrogate-assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules". IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2016.2562674
  • Yan, L., Mei, Y., Ma, H., & Zhang, M. (2016) "Evolutionary web service composition: A graph-based memetic algorithm". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 201-208). IEEE. doi:10.1109/CEC.2016.7743796
  • Wang, J., Xue, B., Gao, X., & Zhang, M. (2016) "A differential evolution approach to feature selection and instance selection". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9810 LNCS (pp. 588-602). Phuket, THAILAND. doi:10.1007/978-3-319-42911-3_49
  • Tran, C. T., Zhang, M., & Andreae, P. (2016) "A Genetic Programming-Based Imputation Method for Classification with Missing Data". In M. Heywood, J. McDermott, M. Castelli, E. Costa, & K. Sim (Eds.), Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science Vol. 9594 (pp. 149-163). Springer. doi:10.1007/978-3-319-30668-1_10
  • Tran, B. N., Zhang, M., & Xue, B. (2016) "A PSO Based Hybrid Feature Selection Algorithm For High-Dimensional Classification". In Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC). Vancouver, BC, Canada: IEEE. doi:10.1109/CEC.2016.7744271
  • Tran, B., Zhang, M., & Xue, B. (2016) "Multiple feature construction in classification on high-dimensional data using GP". In IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). Athens, Greece.
  • Tran, B., Xue, B., Zhang, M., & Nguyen, S. (2016) "Investigation on Particle Swarm Optimisation for Feature Selection on High-dimensional Data: Local Search and Selection Bias". Connection Science, 28(3), 270-294. doi:10.1080/09540091.2016.1185392
  • Tran, B., Xue, B., & Zhang, M. (2016) "Bare-Bone Particle Swarm Optimisation for Simultaneously Discretising and Selecting Features for High-Dimensional Classification". In Applications of Evolutionary Computation 19th European Conference, EvoApplications 2016 Porto, Portugal, March 30 – April 1, 2016 Proceedings, Part I (pp. 701-718). Springer. doi:10.1007/978-3-319-31204-0_45
  • Tran, B., Xue, B., & Zhang, M. (2016) "A PSO Based Hybrid Feature Selection Algorithm For High-Dimensional Classification". In Unknown Conference (pp. 3801-3808).
  • Tan, B., Mei, Y., Ma, H., & Zhang, M. (2016) "Particle Swarm Optimization for Multi-Objective Web Service Location Allocation". Evolutionary Computation in Combinatorial Optimization. EvoCOP 2016. Lecture Notes in Computer Science, 9595. Retrieved from http://homepages.ecs.vuw.ac.nz/~yimei/papers/EvoStar2016-boxiong.
  • Tan, B., Mei, Y., Ma, H., & Zhang, M. (2016) "Particle swarm optimization for multi-objective web service location allocation". In Proceedings of the European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP) (pp. 219-234). Springer. doi:10.1007/978-3-319-30698-8_15
  • Tan, B., Ma, H., & Zhang, M. (2016) "Optimization of location allocation of web services using a modified non-dominated sorting genetic algorithm". In Artificial Life and Computational Intelligence Second Australasian Conference, ACALCI 2016 (pp. 246-257). Canberra: Springer. doi:10.1007/978-3-319-28270-1_21
  • Tan, B., Ma, H., & Zhang, M. (2016) "Optimization of Location Allocation of Web Services Using a Modified Non-dominated Sorting Genetic Algorithm". In T. Ray, R. Sarker, & X. Li (Eds.), Artificial Life and Computational Intelligence. Lecture Notes in Computer Science Vol. 9592. Springer.
  • Squillero, G., Burelli, P., Bacardit, J., Brabazon, A., Cagnoni, S., Cotta, C., . . . Zhang, M. (2016) "Applications of evolutionary computation: 19th European conference, Evoapplications 2016 Porto, Portugal, March 30 – April 1, 2016 proceedings, part II". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9598.
  • Squillero, G., Burelli, P., Bacardit, J., Brabazon, A., Cagnoni, S., Cotta, C., . . . Zhang, M. (2016) "Applications of evolutionary computation: 19th European conference, evoapplications 2016 Porto, Portugal, march 30 – april 1, 2016 proceedings, part I". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9597.
  • Squillero, G., Bacardit, J., Cagnoni, S., De Falco, I., Divina, F., Esparcia-Alcázar, A. I., . . . Zhang, M. (2016) "Preface (Vol". 9597).
  • Sabar, N. R., Song, A., & Zhang, M. (2016) "A variable local search based memetic algorithm for the load balancing problem in cloud computing". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9597 (pp. 267-282). doi:10.1007/978-3-319-31204-0_18
  • Riley, M., Mei, Y., & Zhang, M. (2016) "Improving job shop dispatching rules via terminal weighting and adaptive mutation in genetic programming". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 3362-3369). IEEE. doi:10.1109/CEC.2016.7744215
  • Poaka, V., Hartmann, S., Ma, H., & Steinmetz, D. (2016) "A link-density-based algorithm for finding communities in social networks". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9975 LNCS (pp. 76-85). doi:10.1007/978-3-319-47717-6_7
  • Peng, Y., Chen, G., Zhang, M., & Pang, S. (2016) "Generalized compatible function approximation for policy gradient search". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9947 LNCS (pp. 615-622). Kyoto, Japan. doi:10.1007/978-3-319-46687-3_68
  • Park, J., Mei, Y., Nguyen, S., Chen, G., Johnston, M., & Zhang, M. (2016) "Genetic programming based hyper-heuristics for dynamic job shop scheduling: cooperative coevolutionary approaches". In Proceedings of the European Conference on Genetic Programming (EuroGP) (pp. 115-132). Springer. doi:10.1007/978-3-319-30668-1_8
  • Park, J., Mei, Y., Chen, G., & Zhang, M. (2016) "Niching genetic programming based hyper-heuristic approach to dynamic job shop scheduling: an investigation into distance metrics". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 109-110). ACM. doi:10.1145/2908961.2908985
  • Nguyen, S., Mei, Y., & Zhang, M. (2016) "Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation". Genetic Programming and Evolvable Machines.
  • Nguyen, S., Mei, Y., Ma, H., Chen, A., & Zhang, M. (2016) "Evolutionary scheduling and combinatorial optimisation: Applications, challenges, and future directions". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 3053-3060). IEEE. doi:10.1109/CEC.2016.7744175
  • Nguyen, H. B., Xue, B., & Zhang, M. (2016) "A subset similarity guided method for multi-objective feature selection". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9592 (pp. 298-310). doi:10.1007/978-3-319-28270-1_25
  • Nguyen, H. B., Xue, B., & Andreae, P. (2016) "Mutual information estimation for filter based feature selection using particle swarm optimization". In Lecture Notes in Computer Science : Applications of Evolutionary Computation 19th European Conference, EvoApplications 2016 Vol. 9597 (pp. 719-736). doi:10.1007/978-3-319-31204-0_46
  • Mirghasemi, S., Rayudu, R., & Zhang, M. (2016) "A new modification of fuzzy C-means via particle swarm optimization for noisy image segmentation". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9592 (pp. 147-159). doi:10.1007/978-3-319-28270-1_13
  • Mei, Y., Zhang, M., & Nyugen, S. (2016) "Feature selection in evolving job shop dispatching rules with genetic programming". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 365-372). ACM. doi:10.1145/2908812.2908822
  • Mei, Y., & Zhang, M. (2016) "A comprehensive analysis on reusability of GP-evolved job shop dispatching rules". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 3590-3597). IEEE. doi:10.1109/CEC.2016.7744244
  • Mei, Y., Xue, B., & Zhang, M. (2016) "Fast bi-objective feature selection using entropy measures and bayesian inference". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 469-476). ACM. doi:10.1145/2908812.2908823
  • Mei, Y., Salim, F. D., & Li, X. (2016) "Efficient meta-heuristics for the multi-objective time-dependent orienteering problem". European Journal of Operational Research, 254, 443-457. doi:10.1016/j.ejor.2016.03.053
  • Mei, Y., Omidvar, M. N., Li, X., & Yao, X. (2016) "A competitive divide-and-conquer algorithm for unconstrained large-scale black-box optimization". ACM Transactions on Mathematical Software (TOMS), 42, 13:1-13:24.
  • Mei, Y., Omidvar, M. N., Li, X., & Yao, X. (2016) "A competitive divide-and-conquer algorithm for unconstrained large-scale black-box optimization". ACM Transactions on Mathematical Software, 42, 13:1-24. doi:10.1145/2791291
  • Mei, Y., Li, X., & Yao, X. (2016) "On investigation of interdependence between sub-problems of the travelling thief problem". Soft Computing, 20, 157-172. doi:10.1007/s00500-014-1487-2
  • Masood, A., Mei, Y., Chen, G., & Zhang, M. (2016) "Many-objective genetic programming for job-shop scheduling". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 209-216). IEEE. doi:10.1109/CEC.2016.7743797
  • Ma, J., Xue, B., & Zhang, M. (2016) "A profile-based authorship attribution approach to forensic identification in Chinese online messages". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9650 (pp. 33-52). doi:10.1007/978-3-319-31863-9_3
  • Liu, J., Mei, Y., & Li, X. (2016) "An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization". IEEE Transactions on Evolutionary Computation, 20, 666-681. doi:10.1109/TEVC.2015.2503422
  • Limtrairut, P., Marshall, S., & Andreae, P. (2016) "Know the mobile learning application users: Transactional distance perspective". In CSEDU 2016 - Proceedings of the 8th International Conference on Computer Supported Education Vol. 2 (pp. 378-387). doi:10.5220/0005791303780387
  • Liang, Y., Zhang, M., & Browne, W. N. (2016) "Multi-objective genetic programming for figure-ground image segmentation". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9592 (pp. 134-146). doi:10.1007/978-3-319-28270-1_12
  • Li, L., Zhang, F. F., Chu, X., & Niu, B. (2016) "Modified brain storm optimization algorithms based on topology structures". In Unknown Conference Vol. 9713 LNCS (pp. 408-415). doi:10.1007/978-3-319-41009-8_44
  • Lensen, A., Al-Sahaf, H., Zhang, M., & Xue, B. (2016) "Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data". In Proceedings of the 19th European Conference on Genetic Programming (EuroGP 2016) (Vol. 9594, pp. 51-67). Springer. doi:10.1007/978-3-319-30668-1_4
  • Kumar, S., Gao, X., Welch, I., & Mansoori, M. (2016) "A machine learning based web spam filtering approach". In 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA) (pp. 973-980). IEEE.
  • Kumar, S., Gao, X., & Welch, I. (2016) "RETRACTED CHAPTER: Co-clustering for Dual Topic Models". In Australasian Joint Conference on Artificial Intelligence (pp. 390-402). Springer, Cham.
  • Kumar, S., Gao, X., & Welch, I. (2016) "Novel features for web spam detection". In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 593-597). IEEE.
  • Kumar, S., Gao, X., & Welch, I. (2016) "Learning under data shift for domain adaptation: A model-based co-clustering transfer learning solution". In Pacific Rim Knowledge Acquisition Workshop (pp. 43-54). Springer, Cham.
  • Karunakaran, D., Chen, G., & Zhang, M. (2016) "Parallel multi-objective job shop scheduling using genetic programming". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9592 (pp. 234-245). doi:10.1007/978-3-319-28270-1_20
  • Iqbal, M., Xue, B., & Zhang, M. (2016) "Reusing extracted knowledge in genetic programming to solve complex texture image classification problems". In J. Baily, L. Khan, T. Washio, G. Dobbie, J. Z. Huang, & R. Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9652 LNAI (pp. 117-129). Auckland: Springer. doi:10.1007/978-3-319-31750-2_10
  • Hartmann, S., & Ma, H. (2016) "Preface". In Unknown Book (Vol. 9827 LNCS, pp. V-VI). doi:10.1007/978-3-319-44403-1
  • Ghifary, M., Kleijn, W. B., Zhang, M., Balduzzi, D., & Li, W. (2016) "Deep reconstruction-classification networks for unsupervised domain adaptation". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9908 LNCS, pp. 597-613). doi:10.1007/978-3-319-46493-0_36
  • Ghifary, M., Balduzzi, D., Kleijn, B., & Zhang, M. (2016) "Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization". IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 pages. doi:10.1109/TPAMI.2016.2599532
  • da Silva, A. S., Mei, Y., Ma, H., & Zhang, M. (2016) "Particle swarm optimisation with sequence-like indirect representation for web service composition". In Proceedings of the European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP) (pp. 202-218). Springer. doi:10.1007/978-3-319-30698-8_14
  • da Silva, A. S., Mei, Y., Ma, H., & Zhang, M. (2016) "A memetic algorithm-based indirect approach to web service composition". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). doi:10.1109/CEC.2016.7744218
  • Crabtree, D., Gao, X., & Andreae, P. (2016) "Query aspects approach to web search". Web Intelligence, 14(3), 173-197. doi:10.3233/WEB-160338
  • Consoli, P. A., Mei, Y., Minku, L. L., & Yao, X. (2016) "Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis". Soft Computing, 20, 3889-3914. doi:10.1007/s00500-016-2126-x
  • Barddal, J. P., Gomes, H. M., Granatyr, J., De Souza Britto, A., & Enembreck, F. (2016) "Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning". In Proceedings - International Conference on Pattern Recognition Vol. 0 (pp. 2186-2191). doi:10.1109/ICPR.2016.7899960
  • Barddal, J. P., Gomes, H. M., Enembreck, F., Pfahringer, B., & Bifet, A. (2016) "On dynamic feature weighting for feature drifting data streams". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9852 LNAI (pp. 129-144). doi:10.1007/978-3-319-46227-1_9
  • Barddal, J. P., Gomes, H. M., De Souza Britto, A., & Enembreck, F. (2016) "A benchmark of classifiers on feature drifting data streams". In Proceedings - International Conference on Pattern Recognition Vol. 0 (pp. 2180-2185). doi:10.1109/ICPR.2016.7899959
  • Alvarez, I. M., Browne, W. N., & Zhang, M. (2016) "Compaction for code fragment based learning classifier systems". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9592 (pp. 41-53). doi:10.1007/978-3-319-28270-1_4
  • Al-Sahaf, H., Zhang, M., & Johnston, M. (2016) "Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances". Evolutionary Computation (Journal, MIT Press), 24, 143-182. doi:10.1162/EVCO_a_00146
  • Ahmed, S., Zhang, M., Peng, L., & Xue, B. (2016) "A multi-objective genetic programming biomarker detection approach in mass spectrometry data". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9597 (pp. 106-122). doi:10.1007/978-3-319-31204-0_8
  • Wahid, A., Gao, X., & Andreae, P. (2015) "Multi-objective clustering ensemble for high-dimensional data based on Strength Pareto Evolutionary Algorithm (SPEA-II)". In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015. doi:10.1109/DSAA.2015.7344795
  • Nekooei, S. M., Chen, G., & Rayudu, R. K. (2015) "A fuzzy logic based cross-layer mechanism for medium access control in WBAN". In Personal, Indoor and Mobile Radio Communications, PIMRC, 2015 IEEE 26th International Symposium on Vol. 2015-December (pp. 1094-1099). Online. doi:10.1109/PIMRC.2015.7343461
  • Marzukhi, S., Browne, W. N., & Zhang, M. (2015) "An on-line Pittsburgh LCS for the Three-Cornered Coevolution Framework". Evolutionary Intelligence, 8(4), 185-201. doi:10.1007/s12065-015-0133-y
  • Ghifary, M., Balduzzi, D., Kleijn, W. B., & Zhang, M. (2015) "Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization". __. doi:10.48550/arxiv.1510.04373
  • Li, L., Zhang, F. F., Liu, C., & Niu, B. (2015) "A hybrid Artificial Bee Colony algorithm with bacterial foraging optimization". In 2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015 (pp. 127-132). doi:10.1109/CYBER.2015.7287922
  • Chowdhury, S., Verma, B., Tom, M., & Zhang, M. (2015) "Pixel characteristics based feature extraction approach for roadside object detection". In Proceedings of the International Joint Conference on Neural Networks Vol. 2015-September. doi:10.1109/IJCNN.2015.7280599
  • Rada-Vilela, J., Johnston, M., & Zhang, M. (2015) "Population statistics for particle swarm optimization: Single-evaluation methods in noisy optimization problems". Soft Computing, 19(9), 2691-2716. doi:10.1007/s00500-014-1438-y
  • Yu, Y., Ma, H., & Zhang, M. (2015) "F-MOGP: A novel many-objective evolutionary approach to QoS-aware data intensive web service composition". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2843-2850). doi:10.1109/CEC.2015.7257242
  • Tran, C. T., Andreae, P., & Zhang, M. (2015) "Impact of imputation of missing values on Genetic programming based multiple feature construction for classification". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2398-2405). doi:10.1109/CEC.2015.7257182
  • Nguyen, S., Zhang, M., & Tan, K. C. (2015) "Enhancing genetic programming based hyper-heuristics for dynamic multi-objective job shop scheduling problems". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2781-2788). doi:10.1109/CEC.2015.7257234
  • Koleejan, C., Xue, B., & Zhang, M. (2015) "Code coverage optimisation in genetic algorithms and particle swarm optimisation for automatic software test data generation". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 1204-1211). doi:10.1109/CEC.2015.7257026
  • Hancer, E., Xue, B., Zhang, M., Karaboga, D., & Akay, B. (2015) "A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2420-2427). doi:10.1109/CEC.2015.7257185
  • Da Silva, A. S., Ma, H., & Zhang, M. (2015) "A GP approach to QoS-aware Web service composition including conditional constraints". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2113-2120). doi:10.1109/CEC.2015.7257145
  • Chen, Q., Xue, B., & Zhang, M. (2015) "Generalisation and domain adaptation in GP with gradient descent for symbolic regression". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 1137-1144). doi:10.1109/CEC.2015.7257017
  • Butler-Yeoman, T., Xue, B., & Zhang, M. (2015) "Particle swarm optimisation for feature selection: A hybrid filter-wrapper approach". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2428-2435). doi:10.1109/CEC.2015.7257186
  • Ghifary, M., Kleijn, W. B., Zhang, M., & Balduzzi, D. (2015) "Domain Generalization for Object Recognition with Multi-task Autoencoders". __. doi:10.48550/arxiv.1508.07680
  • Hancer, E., Xue, B., Karaboga, D., & Zhang, M. (2015) "A binary ABC algorithm based on advanced similarity scheme for feature selection". Applied Soft Computing Journal, 36, 334-348. doi:10.1016/j.asoc.2015.07.023
  • Fu, W., Johnston, M., & Zhang, M. (2015) "Distribution-based invariant feature construction using genetic programming for edge detection". Soft Computing, 19(8), 2371-2389. doi:10.1007/s00500-014-1432-4
  • Wang, D. X., Gao, X., & Andreae, P. (2015) "DIKEA: Exploiting Wikipedia for keyphrase extraction". Web Intelligence, 13(3), 153-165. doi:10.3233/WEB-150318
  • Fu, W., Zhang, M., & Johnston, M. (2015) "Genetic programming for extracting edge features using two blocks". In Conferences in Research and Practice in Information Technology Series Vol. 168 (pp. 141-150).
  • Butler-Yeoman, T., Xue, B., & Zhang, M. (2015) "Particle swarm optimisation for feature selection: A size-controlled approach". In Conferences in Research and Practice in Information Technology Series Vol. 168 (pp. 151-159).
  • Zhang, M., & Cagnoni, S. (2015) "Evolutionary image analysis, signal processing and pattern recognition". In GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference (pp. 473-502). doi:10.1145/2739482.2756566
  • Tran, C. T., Zhang, M., & Andreae, P. (2015) "Multiple imputation for missing data using genetic programming". In GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 583-590). Online. doi:10.1145/2739480.2754665
  • Park, J., Nguyen, S., Zhang, M., & Johnston, M. (2015) "A single population genetic programming based ensemble learning approach to job shop scheduling". In GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference (pp. 1451-1452). doi:10.1145/2739482.2764651
  • Nguyen, S., Zhang, M., & Tan, K. C. (2015) "A dispatching rule based genetic algorithm for order acceptance and scheduling". In GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference (pp. 433-440). doi:10.1145/2739480.2754821
  • Iqbal, M., Browne, W. N., & Zhang, M. (2015) "Improving genetic search in XCS-based classifier systems through understanding the evolvability of classifier rules". Soft Computing, 19(7), 1863-1880. doi:10.1007/s00500-014-1369-7
  • Xue, B., Zhang, M., & Browne, W. N. (2015) "A comprehensive comparison on evolutionary feature selection approaches to classification". International Journal of Computational Intelligence and Applications, 14(2), 23 pages. doi:10.1142/S146902681550008X
  • Rada-Vilela, J., Johnston, M., & Zhang, M. (2015) "Population statistics for particle swarm optimization: Hybrid methods in noisy optimization problems". Swarm and Evolutionary Computation, 22, 15-29. doi:10.1016/j.swevo.2015.01.003
  • Gomes, H. M., Barddal, J. P., & Enembreck, F. (2015) "Pairwise combination of classifiers for ensemble learning on data streams". In Proceedings of the ACM Symposium on Applied Computing Vol. 13-17-April-2015 (pp. 941-946). doi:10.1145/2695664.2695754
  • Barddal, J. P., Gomes, H. M., & Enembreck, F. (2015) "SNCStream: A social network-based data stream clustering algorithm". In Proceedings of the ACM Symposium on Applied Computing Vol. 13-17-April-2015 (pp. 935-940). doi:10.1145/2695664.2695674
  • Ghifary, M., Kleijn, W. B., Zhang, M., & Balduzzi, D. (2015) "Domain generalization for object recognition with multi-task autoencoders". Proceedings of the IEEE International Conference on Computer Vision, 2015 International Conference on Computer Vision, ICCV 2015, 2551-2559. doi:10.1109/ICCV.2015.293
  • Xu, Z., Xue, B., Shi, X., Zhang, Q., Zhai, W., Yao, J., & Wang, Y. (2015) "Sliding Speed and Load Dependence of Tribological Properties of Ti3SiC2/TiAl Composite". Tribology Transactions, 58(1), 87-96. doi:10.1080/10402004.2014.951748
  • Xie, J., Mei, Y., & Song, A. (2015) "Evolving self-adaptive tabu search algorithm for storage location assignment problems". In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO) (pp. 779-780). ACM. doi:10.1145/2739482.2764896
  • Xie, J., Mei, Y., Ernst, A. T., Li, X., & Song, A. (2015) "A restricted neighbourhood tabu search for storage location assignment problem". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 2805-2812). IEEE. doi:10.1109/CEC.2015.7257237
  • Wahid, A., Gao, X., & Andreae, P. (2015) "Multi-objective multi-view clustering ensemble based on evolutionary approach". In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 1696-1703). Online. doi:10.1109/CEC.2015.7257091
  • Wahid, A., Gao, X., & Andreae, P. (2015) "A soft subspace clustering method for text data using a probability based feature weighting scheme". In J. Wang, W. Cellary, D. Wang, H. Wang, S. -C. Chen, T. Li, & Y. Zhang (Eds.), Web Information Systems Engineering – WISE 2015 16th International Conference Miami, FL, USA, November 1–3, 2015 Proceedings, Part II Vol. 9419 (pp. 124-138). Miami, Florida, USA. doi:10.1007/978-3-319-26187-4_9
  • Park, J., Nguyen, S., Zhang, M., & Johnston, M. (2015) "Evolving Ensembles of Dispatching Rules Using Genetic Programming for Job Shop Scheduling". In P. Machado, M. I. Heywood, J. McDermott, M. Castelli, P. GarciaSanchez, P. Burelli, . . . K. Sim (Eds.), GENETIC PROGRAMMING (EUROGP 2015) Vol. 9025 (pp. 92-104). Copenhagen, DENMARK: SPRINGER-VERLAG BERLIN. doi:10.1007/978-3-319-16501-1_8
  • Nguyen, H. B., Xue, B., Liu, I., Andreae, P., & Zhang, M. (2015) "Gaussian transformation based representation in particle swarm optimisation for feature selection". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9028 (pp. 541-553). doi:10.1007/978-3-319-16549-3_44
  • Mora, A. M., Squillero, G., Agapitos, A., Burelli, P., Bush, W. S., Cagnoni, S., . . . Zhang, M. (2015) "Applications of evolutionary computation: 18th European Conference, EvoApplications 2015 Copenhagen, Denmark, April 8–10, 2015 proceedings". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9028. doi:10.1007/978-3-319-16549-3
  • Mirghasemi, S., Rayudu, R., & Zhang, M. (2015) "A heuristic solution for noisy image segmentation using Particle Swarm Optimization and Fuzzy clustering". In 2015 7th International Joint Conference on Computational Intelligence (IJCCI 2015) Vol. 1 (pp. 17-27). Lisbon, Portugal.
  • Mei, Y., Li, X., Salim, F., & Yao, X. (2015) "Heuristic evolution with genetic programming for traveling thief problem". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 2753-2760). IEEE. doi:10.1109/CEC.2015.7257230
  • Marshall, R. J., Johnston, M., & Zhang, M. (2015) "Hyper-heuristic operator selection and acceptance criteria". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9026 (pp. 99-113). doi:10.1007/978-3-319-16468-7_9
  • Ma, H., Wang, A., & Zhang, M. (2015) "A Hybrid Approach Using Genetic Programming and Greedy Search for QoS-Aware Web Service Composition". In A. Hameurlain, J. Kung, R. Wagner, H. Decker, L. Lhotska, & S. Link (Eds.), Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII : Special Issue on Databaseand Expert-Systems Applications Vol. 8980 (pp. 180-205). Berlin Heidelberg: SPRINGER-VERLAG. doi:10.1007/978-3-662-46485-4_7
  • Liang, Y., Zhang, M., & Browne, W. N. (2015) "A supervised figure-ground segmentation method using genetic programming". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9028 (pp. 491-503). doi:10.1007/978-3-319-16549-3_40
  • Lensen, A., Al-Sahaf, H., Zhang, M., & Xue, B. (2015) "A Hybrid Genetic Programming Approach to Feature Detection and Image Classification". In Proceedings of the 30th International Conference on Image and Vision Computing New Zealand (IVCNZ 2015) (pp. 1-6). IEEE. doi:10.1109/IVCNZ.2015.7761564
  • Lensen, A., Al-Sahaf, H., Zhang, M., & Verma, B. (2015) "Genetic Programming for Algae Detection in River Images". In Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015) (pp. 2468-2475). IEEE. doi:10.1109/CEC.2015.7257191
  • Hunt, R., Johnston, M., & Zhang, M. (2015) "Using local search to evaluate dispatching rules in dynamic job shop scheduling". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9026 (pp. 222-233). doi:10.1007/978-3-319-16468-7_19
  • Hartman, S., Ma, H., & Vechsamutvaree, P. (2015) "Providing ontology-based privacy-aware data access through web services". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9382 (pp. 74-85). doi:10.1007/978-3-319-25747-1_8
  • Gomes, H. M., De Carvalho, D. R., Zubieta, L., Barddal, J. P., & Malucelli, A. (2015) "On the discovery of time distance constrained temporal association rules". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9490 (pp. 510-519). doi:10.1007/978-3-319-26535-3_58
  • Gan, X., Liu, L., Niu, B., Tan, L. J., Zhang, F. F., & Liu, J. (2015) "SRBFOs for solving the heterogeneous fixed fleet vehicle routing problem". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9226 (pp. 725-732). doi:10.1007/978-3-319-22186-1_72
  • Fang, W., Li, X., Zhang, M., & Hu, M. (2015) "Nature-Inspired Algorithms for Real-World Optimization Problems". Journal of Applied Mathematics, 2015. doi:10.1155/2015/359203
  • De Souza, A. J., Borges, A. P., Gomes, H. M., Barddal, J. P., & Enembreck, F. (2015) "Applying ensemble-based online learning techniques on crime forecasting". In ICEIS 2015 - 17th International Conference on Enterprise Information Systems, Proceedings Vol. 1 (pp. 17-24). doi:10.5220/0005335700170024
  • da Silva, A. S., Ma, H., & Zhang, M. (2015) "GraphEvol: A graph evolution technique for web service composition". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9262 (pp. 134-142). doi:10.1007/978-3-319-22852-5_12
  • Cotterell, K., Welch, I., & Chen, A. (2015) "An android security policy enforcement tool". International Journal of Electronics and Telecommunications, 61, 311-320.
  • Chen, G., Douch, C. I. J., & Zhang, M. (2015) "Using learning classifier systems to learn stochastic decision policies". IEEE Transactions on Evolutionary Computation, 19(6), 885-902. doi:10.1109/TEVC.2015.2415464
  • Chen, G., Douch, C., Zhang, M., & Pang, S. (2015) "Reinforcement Learning in Continuous Spaces by Using Learning Fuzzy Classifier Systems". In S. Arik, T. Huang, W. K. Lai, & Q. Liu (Eds.), NEURAL INFORMATION PROCESSING, PT II Vol. 9490 (pp. 320-328). Istanbul, TURKEY: SPRINGER INT PUBLISHING AG. doi:10.1007/978-3-319-26535-3_37
  • Barddal, J. P., Gomes, H. M., & Enembreck, F. (2015) "Analyzing the impact of feature drifts in streaming learning". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9489 (pp. 21-28). doi:10.1007/978-3-319-26532-2_3
  • Barddal, J. P., Gomes, H. M., & Enembreck, F. (2015) "Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory". International Journal of Natural Computing Research, 5(1), 26-41. doi:10.4018/ijncr.2015010102
  • Barddal, J. P., Gomes, H. M., & Enembreck, F. (2015) "A complex network-based anytime data stream clustering algorithm". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9489 (pp. 615-622). doi:10.1007/978-3-319-26532-2_68
  • Al-Sahaf, H., Zhang, M., Johnston, M., & Verma, B. (2015) "Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification". In Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015) (pp. 2460-2467). IEEE. doi:10.1109/CEC.2015.7257190
  • Al-Sahaf, H., Zhang, M., & Johnston, M. (2015) "Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction". In Proceedings of the 2015 Genetic and Evolutionary Computation Conference (GECCO 2015) (pp. 975-982). ACM. doi:10.1145/2739480.2754661
  • Bhowan, U., Johnston, M., Zhang, M., & Yao, X. (2014) "Reusing Genetic Programming for Ensemble Selection in Classification of Unbalanced Data". IEEE Transactions on Evolutionary Computation, 18(6), 893-908. doi:10.1109/TEVC.2013.2293393
  • Ghifary, M., Kleijn, W. B., & Zhang, M. (2014) "Domain Adaptive Neural Networks for Object Recognition". __. doi:10.48550/arxiv.1409.6041
  • Fu, W., Johnston, M., & Zhang, M. (2014) "Distribution-based invariant feature construction using genetic programming for edge detection". Soft Computing. doi:10.1007/s00500-014-1432-4
  • Iqbal, M., Browne, W., & Zhang, M. (2014) "Reusing Building Blocks of Extracted Knowledge to Solve Complex, Large-Scale Boolean Problems". IEEE Transactions on Evolutionary Computation, 18(4), 465-480. doi:10.1109/TEVC.2013.2281537
  • Fu, W., Johnston, M., & Zhang, M. (2014) "Low-Level Feature Extraction for Edge Detection Using Genetic Programming". IEEE Transactions on Cybernetics, 44(8), 1459-1472. doi:10.1109/TCYB.2013.2286611
  • Bell, T., Andreae, P., & Robins, A. (2014) "A Case Study of the Introduction of Computer Science in NZ Schools". ACM Transactions on Computing Education, 14(2), 31 pages. doi:10.1145/2602485
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2014) "Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming". IEEE Transactions on Evolutionary Computation, 18(2), 193-208. doi:10.1109/TEVC.2013.2248159
  • Yu, Y., Ma, H., & Zhang, M. (2014) "A Hybrid GP-Tabu Approach to QoS-aware Data Intensive Web Service Composition". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 106-118). Dunedin: Springer.
  • Yu, Y., Ma, H., & Zhang, M. (2014) "A Genetic Programming Approach to Distributed QoS-aware Web Service Composition". In Proceedings of 2014 IEEE Congress on Evolutionary Computation (pp. 1840-1846). Beijing, China: IEEE Press.
  • Xue, B., Zhang, M., & Browne, W. (2014) "Particle Swarm Optimisation for Feature Selection in Classification: Novel Initialisation and Updating Mechanisms". Applied Soft Computing, 18, 261-276. doi:10.1016/j.asoc.2013.09.018
  • Xue, B., Xu, Z., Zhang, Q., Shi, X., Wang, M., Zhai, W., . . . Song, S. (2014) "Tribological properties of TiAl-Ti3SiC2 composites". Journal Wuhan University of Technology, Materials Science Edition, 29(2), 256-263. doi:10.1007/s11595-014-0904-9
  • Xue, B., Tong, B., & Jia, Q. (2014) "The influence of different thermo-mechanical histories on the crystalline morphology of PET Fiber/iPP composites". In Proceedings - 2014 6th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2014 (pp. 708-711). doi:10.1109/ICMTMA.2014.175
  • Xue, B., Qin, A., & Zhang, M. (2014) "An Archive based Particle Swarm Optimisation for Feature Selection in Classification". In Proceedings of 2014 IEEE Congress on Evolutionary Computation (pp. 3119-3126). Beijing: IEEE Press.
  • Xue, B., Nguyen, S., & Zhang, M. (2014) "A New Binary Particle Swarm Optimisation Algorithm for Feature Selection". In Proceedings of the 16th European Conference on the Applications of Evolutionary Computation (EvoApplications 2014) (pp. 501-513). Berlin: Springer.
  • Xue, B., Fu, W., & Zhang, M. (2014) "Multi-Objective Feature Selection in Classification: A Differential Evolution Approach". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 516-528). Berlin: Springer.
  • Xue, B., Fu, W., & Zhang, M. (2014) "Differential evolution (DE) for Multi-objective Feature Selection in Classification". In Proceedings of 2014 Genetic and Evolutionary Computation Conference (GECCO 2014 Companion) (pp. 83-84). Vancouver, Canada: ACM Press.
  • Xue, B., Cervante, L., Shang, L., Browne, W., & Zhang, M. (2014) "Binary PSO and rough set theory for feature selection: a multi-objective filter based approach". International Journal of Computational Intelligence and Applications, 13(2), 1450009-1-1450009-34. doi:10.1142/S1469026814500096
  • Xie, J., Mei, Y., Ernst, A. T., Li, X., & Song, A. (2014) "Scaling up solutions to storage location assignment problems by genetic programming". In Proceedings of the Asia-Pacific Conference on Simulated Evolution and Learning (SEAL) (pp. 691-702). Springer. doi:10.1007/978-3-319-13563-2_58
  • Xie, J., Mei, Y., Ernst, A. T., Li, X., & Song, A. (2014) "A genetic programming-based hyper-heuristic approach for storage location assignment problem". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 3000-3007). IEEE. doi:10.1109/CEC.2014.6900604
  • Wahid, A., Gao, X., & Andreae, P. (2014) "Multi-view clustering of web documents using multi-objective genetic algorithm". In 2014 IEEE Congress on Evolutionary Computation (CEC 2014) (pp. 2625-2632). Beijing, China: IEEE. doi:10.1109/CEC.2014.6900586
  • Tran, B., Xue, B., & Zhang, M. (2014) "Overview of Particle Swarm Optimisation for Feature Selection in Classification". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL), Dunedin, New Zealand (pp. 605-617). Springer International Publishing. doi:10.1007/978-3-319-13563-2_51
  • Tran, B., Xue, B., & Zhang, M. (2014) "Improved PSO for Feature Selection on High-Dimensional Datasets". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL), Dunedin, New Zealand (pp. 503-515). Springer International Publishing. doi:10.1007/978-3-319-13563-2_43
  • Tirumala, S. S., Pang, S., & Chen, G. (2014) "Quantum inspired evolutionary algorithm by representing candidate solution as normal distribution". In C. K. Loo, K. S. Yap, K. W. Wong, A. Teoh, & K. Huang (Eds.), Neural Information Processing 21st International Conference, ICONIP 2014 Kuching, Malaysia, November 3–6, 2014 Proceedings, Part III Vol. 8836 (pp. 308-316). Berlin: Springer. doi:10.1007/978-3-319-12643-2_38
  • Tirumala, S. S., Chen, G., & Pang, S. (2014) "Quantum Inspired Evolutionary Algorithm by Representing Candidate Solution as Normal Distribution". In C. K. Loo, K. S. Yap, K. W. Wong, A. Teoh, & K. Huang (Eds.), NEURAL INFORMATION PROCESSING, ICONIP 2014, PT III Vol. 8836 (pp. 308-316). Kuching, MALAYSIA: SPRINGER-VERLAG BERLIN. Retrieved from http://gateway.webofknowledge.
  • Sawczuk da Silva, A., Ma, H., & Zhang, M. (2014) "A Graph-Based Particle Swarm Optimisation Approach to QoS-Aware Web Service Composition and Selection". In Proceedings of 2014 IEEE Congress on Evolutionary Computation (pp. 3127-3134). Beijing, China: IEEE Press.
  • Sawczuk da Silva, A., Ma, H., & Zhang, M. (2014) "A GP Approach to QoS-Aware Web Service Composition and Selection". In Simulated Evolution and Learning 10th International Conference, SEAL 2014 Dunedin, New Zealand, December 15-18, 2014 Proceedings (pp. 180-191). Dunedin: Springer.
  • Sawczuk da Silva, A., Gao, X., & Andreae, P. (2014) "Wallace: Incorporating Search into Chatting". In Proceedings of 13th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2014, Gold Coast, QLD, Australia, 2014, Trends in Artificial Intelligence (pp. 842-848).
  • Rahman, I., Hollitt, C., & Zhang, M. (2014) "Information Divergence Based Saliency Detection with a Global Center-Surround Mechanism". In Proceedings of 22nd International Conference on Pattern Recognition (ICPR 2014) (pp. 3428-3433). Stockholm: IEEE Press. doi:10.1109/ICPR.2014.590
  • Rada-Vilela, J., Johnston, M., & Zhang, M. (2014) "Deception, blindness and disorientation in particle swarm optimization applied to noisy problems". Swarm Intelligence, 8, 247-273.
  • Rada-Velila, J., Johnston, M., & Zhang, M. (2014) "Population Statistics for Particle Swarm Optimization: Resampling Methods in Noisy Optimization Problems". Swarm and Evolutionary Computation, 17, 37-59. doi:10.1016/j.swevo.2014.02.004
  • Park, J., Nguyen, S., Zhang, M., & Johnston, M. (2014) "Enhancing Heuristics for Order Acceptance and Scheduling using Genetic Programming". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 723-734). Berlin: Springer.
  • Omidvar, M. N., Mei, Y., & Li, X. (2014) "Optimal Decomposition of Large-Scale Separable Continuous Functions for Cooperative Co-evolutionary Algorithms". In Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC2014) (pp. 1305-1312). IEEE.
  • Omidvar, M. N., Mei, Y., & Li, X. (2014) "Effective decomposition of large-scale separable continuous functions for cooperative co-evolutionary algorithms". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 1305-1312). IEEE. doi:10.1109/CEC.2014.6900420
  • Omidvar, M. N., Li, X., Mei, Y., & Yao, X. (2014) "Cooperative co-evolution with differential grouping for large scale optimization". IEEE Transactions on Evolutionary Computation, 18, 378-393. doi:10.1109/TEVC.2013.2281543
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. (2014) "Selection Schemes in Surrogate-assisted Genetic Programming for Job Shop Scheduling". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 656-667). Berlin: Springer.
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. (2014) "Genetic Programming for Evolving Due-Date Assignment Models in Job Shop Environments". Evolutionary Computation, 22(1), 105-138. doi:10.1162/EVCO_a_00105
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. (2014) "Automatic Programming via Iterated Local Search for Dynamic Job Shop Scheduling". IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2014.2317488
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. (2014) "Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming". IEEE Transactions on Evolutionary Computation, 18, 193-208. doi:10.1109/TEVC.2013.2248159
  • Nguyen, S., Zhang, M., & Johnston, M. (2014) "Enhancing Branch-and-Bound Algorithms for Order Acceptance and Scheduling with Genetic Programming". In Proceedings of the 17th European Conference on Genetic Programming (EuroGP 2014) (pp. 124-136). Berlin: Springer.
  • Nguyen, S., Zhang, M., & Johnston, M. (2014) "A Sequential Genetic Programming Method to Learn Forward Construction Heuristics for Order Acceptance and Scheduling". In Proceedings of 2014 IEEE Congress on Evolutionary Computation (pp. 1824-1831). Beijing, China: IEEE Press.
  • Nguyen, H., Xue, B., Liu, I., & Zhang, M. (2014) "PSO and Statistical Clustering for Feature Selection: A New Representation". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 569-581). Berlin: Springer.
  • Nguyen, H., Xue, B., Liu, I., & Zhang, M. (2014) "PSO and Statistical Clustering for Feature Selection: A New Representation". In Simulated Evolution and Learning (pp. 569-581).
  • Nguyen, H., Xue, B., Liu, I., & Zhang, M. (2014) "Filter Based Backward Elimination in Wrapper Based PSO for Feature Selection in Classification". In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 3111-3118).
  • Nguyen, H., Xue, B., Liu, I., & Zhang, M. (2014) "Filter based Backward Elimination in Wrapper based PSO for Feature Selection in Classification". In Proceedings of 2014 IEEE Congress on Evolutionary Computation (pp. 3111-3118). Beijing: IEEE Press.
  • Mei, Y., Li, X., & Yao, X. (2014) "Variable neighborhood decomposition for large scale capacitated arc routing problem". In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 1313-1320). IEEE.
  • Mei, Y., Li, X., & Yao, X. (2014) "Variable neighborhood decomposition for large scale capacitated arc routing problem". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 1313-1320). IEEE. doi:10.1109/CEC.2014.6900305
  • Mei, Y., Li, X., & Yao, X. (2014) "Improving efficiency of heuristics for the large scale traveling thief problem". In Asia-Pacific Conference on Simulated Evolution and Learning (pp. 631-643). Springer.
  • Mei, Y., Li, X., & Yao, X. (2014) "Improving efficiency of heuristics for the large scale traveling thief problem". In Unknown Book (pp. 631-643). doi:10.1007/978-3-319-13563-2_53
  • Mei, Y., Li, X., & Yao, X. (2014) "Cooperative coevolution with route distance grouping for large-scale capacitated arc routing problems". IEEE Transactions on Evolutionary Computation, 18, 435-449. doi:10.1109/TEVC.2013.2281503
  • McLeod, P., Verma, B., & Zhang, M. (2014) "Optimizing configuration of neural ensemble network for breast cancer diagnosis". In Proceedgins of the 2014 International Joint Conference on Neural Networks (IJCNN 2014) (pp. 1087-1092). Beijing: IEEE.
  • Marzukhi, S., Browne, W., & Zhang, M. (2014) "Three-Cornered Coevolution Learning Classifier Systems for Classification Tasks". In Proceedings of 2014 Genetic and Evolutionary Computation Conference (GECCO 2014) (pp. 549-556). Vancouver, Canada: ACM Press.
  • Marzukhi, S. (2014) "Three-cornered coevolution learning classifier systems for classification". (PhD Thesis).
  • Marshall, R., Johnston, M., & Zhang, M. (2014) "Hyper-Heuristics, Grammatical Evolution and the Capacitated Vehicle Routing Problem". In Proceedings of 2014 Genetic and Evolutionary Computation Conference (GECCO 2014 Companion) (pp. 71-72). Vancouver, Canada: ACM Press.
  • Marshall, R., Johnston, M., & Zhang, M. (2014) "Developing a Hyper-heuristic using Grammatical Evolution and the Capacitated Vehicle Routing Problem". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 668-679). Berlin: Springer.
  • Marshall, R., Johnston, M., & Zhang, M. (2014) "A Comparison between Two Evolutionary Hyper-heuristics for Combinatorial Optimisation". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 618-630). Berlin: Springer.
  • Ma, H., & Schewe, K. D. (2014) "Query handling in geometric conceptual modelling". In T. Tokuda, Y. Kiyoki, H. Jaakkola, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXV (Vol. 260, pp. 174-189). IOP. doi:10.3233/978-1-61499-361-2-174
  • Liang, Y., Zhang, M., & Browne, W. (2014) "Image Segmentation: A Survey of Methods based on Evolutionary Computation". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 847-859). Berlln: Springer.
  • Lewis, J. P., Anjyo, K., Rhee, T., Zhang, M., Pighin, F., & Deng, Z. (2014) "Practice and Theory of Blendshape Facial Models". In State of the Art Reports (pp. 20 pages). Strasbourg/France. doi:10.2312/egst.20141042
  • Lewis, J., Anjyo, K., Rhee, T., Zhang, M., Pighin, F., & Deng, Z. (2014) "Practice and Theory of Blendshape Facial Models". In S. Lefebvre, & M. Spagnuolo (Eds.), Eurographics 2014 - State of the Art Reports (pp. 119-218). Strasbourg, France: The Eurographics Association. doi:10.2312/egst.20141042
  • Lewis, J., Anjyo, K., Rhee, T., Zhang, M., Pighin, F., & Deng, Z. (2014) "Practice and Theory of Blendshape Facial Models". In S. Lefebvre, & M. Spagnuolo (Eds.), Eurographics 2014 - State of the Art Reports. Strasbourg, France: The Eurographics Association. doi:10.2312/egst.20141042
  • Lane, M., Xue, B., Liu, I., & Zhang, M. (2014) "Gaussian Based Particle Swarm Optimisation and Statistical Clustering for Feature Selection". In Evolutionary Computation in Combinatorial Optimisation (pp. 133-144). Springer Berlin Heidelberg.
  • Lane, M., Xue, B., Liu, I., & Zhang, M. (2014) "Gaussian Based Particle Swarm Optimisation and Statistical Clustering for Feature Selection". In Proceedings of the 14th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP2014) (pp. 133-144). Berlin: Springer.
  • Jabeen, S., Gao, X., & Andreae, P. (2014) "Using Asymmetric Associations for Commonsense Causality Detection". In Lecture Notes in Computer Science (pp. 877-883). Springer International Publishing. doi:10.1007/978-3-319-13560-1_73
  • Jabeen, S., Gao, X., & Andreae, P. (2014) "Using Asymmetric Associations for Commonsense Causality Detection". In PRICAI 2014.
  • Jabeen, S., Gao, X., & Andreae, P. (2014) "Probabilistic Associations as a Proxy for Semantic Relatedness". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8786, 512-522. doi:10.1007/978-3-319-11749-2_38
  • Jabeen, S., Gao, X., & Andreae, P. (2014) "Probabilistic Associations as a Proxy for Semantic Relatedness". In WISE 2014 Vol. 8786 (pp. 512-522). the proceedings of the 15th International Conference on Web Information Systems Engineering: Springer. doi:10.1007/978-3-319-11746-1
  • Jabeen, S., Gao, X., & Andreae, P. (2014) "A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features". In B. Benatallah, A. Bestavros, Y. Manolopoulos, A. Vakali, & Y. Zhang (Eds.), Web Information Systems Engineering – WISE 2014 15th International Conference Thessaloniki, Greece, October 12-14, 2014 Proceedings, Part I Vol. 8786 (pp. 523-533). Cham Heidelberg New York Dordrecht London. doi:10.1007/978-3-319-11749-2_39
  • Iqbal, M., Naqvi, S., Browne, W., Hollitt, C., & Zhang, M. (2014) "Salient Object Detection Using Learning Classifier Systems that Compute Action Mappings". In C. Igel (Ed.), GECCO’14, Proceedings of the 2014 Genetic and Evolutionary Computation Conference (pp. 525-532). New York, NY: The Association for Computing Machinery, Inc. (ACM). Retrieved from https://dl.acm.org/citation.
  • Hunt, R., Johnston, M., & Zhang, M. (2014) "Evolving Machine-Specific Dispatching Rules for a Two-Machine Job Shop using Genetic Programming". In Proceedings of 2014 IEEE Congress on Evolutionary Computation (pp. 618-625). Beijing: IEEE Press.
  • Hunt, R., Johnston, M., & Zhang, M. (2014) "Evolving 'Less-Myopic' Scheduling Rules for Dynamic Job Shop Scheduling with Genetic Programming". In Proceedings of 2014 Genetic and Evolutionary Computation Conference (GECCO 2014) (pp. 927-934). Vancouver, Canada: ACM Press.
  • Huang, H., Ma, H., & Zhang, M. (2014) "An enhanced genetic algorithm for web service location-allocation". In Database and Expert Systems Applications 25th International Conference, DEXA 2014 (pp. 223-230). Munich, Germany: Springer. doi:10.1007/978-3-319-10085-2_20
  • Huang, H., Ma, H., & Zhang, M. (2014) "An enhanced genetic algorithm for web service location-allocation". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8645 LNCS (pp. 223-230). doi:10.1007/978-3-319-10085-2_20
  • Hoda, R., & Andreae, P. (2014) "It's Not Them, It's Us! Why Computer Science Fails to Impress Many First Years". In Proceedings of the Sixteenth Australasian Computing Education Conference (ACE2014) (pp. 159-162).
  • Gomes, H. M., & Enembreck, F. (2014) "SAE2: Advances on the social adaptive ensemble classifier for data streams". In Proceedings of the ACM Symposium on Applied Computing (pp. 798-804). doi:10.1145/2554850.2554905
  • Ghifary, M., Kleijn, W. B., & Zhang, M. (2014) "Domain Adaptive Neural Networks for Object Recognition". Unknown Journal, 898-904. doi:10.1007/978-3-319-13560-1_76
  • Ghifary, M., Kleijn, W., & Zhang, M. (2014) "Domain Adaptive Neural Networks for Object Recognition". In Proceedings of the 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2014) (pp. 898-904). Berlin: Springer.
  • Ghifary, M., Kleijn, W., & Zhang, M. (2014) "Deep Hybrid Networks with good out-of-sample object recognition". In 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014) (pp. 5437-5441). Florence, Italy: IEEE Press.
  • Fu, W., Johnston, M., & Zhang, M. (2014) "Unsupervised Learning for Edge Detection using Genetic Programming". In Proceedings of 2014 IEEE Congress on Evolutionary Computation (pp. 117-124). Beijing: IEEE Press.
  • Fu, W., Johnston, M., & Zhang, M. (2014) "Is a Single Image Sufficient for Evolving Edge Features by Genetic Programming?". In Proceedings of the 16th European Conference on the Applications of Evolutionary Computation (EvoApplications 2014), Lecture Notes in Computer Science. Granada, Spain. April 23-25, 2014.
  • Fu, W., Johnston, M., & Zhang, M. (2014) "Automatic Resolution Selection for Edge Detection using Genetic Programming". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 810-821). Berlin: Springer.
  • Esparcia-Alcázar, A. I., Mora, A. M., Agapitos, A., Burelli, P., Bush, W. S., Cagnoni, S., . . . Zincir-Heywood, N. (2014) "Preface (Vol". 8602). doi:10.1007/978-3-662-45523-4
  • Esparcia-Alcazar, A., Mora, A., Burelli, P., Bush, W., et al., Zhang, M., & Zincir-Heywood, N. (2014) "EvoApplications 2014: Proceedings of the 17th European Conference on Applications of Evolutionary Computation". Berlin: Springer.
  • Ebadi, T., Kukenys, I., Browne, W., & Zhang, M. (2014) "Human-Interpretable Feature Pattern Classification System Using Learning Classifier Systems". Evolutionary Computation, 22(4), 629-650. doi:10.1162/EVCO_a_00127
  • Dick, G., Browne, W., Whigham, P., Zhang, M., Bui, L., Ishibuchi, H., . . . Tang, K. (2014) "Simulated Evolution and Learning - 10th International Conference (SEAL 2014)". Berlin: Springer.
  • Dick, G., Browne, W., Whigham, P., & Zhang, M. (2014) "Preface (Vol". 8886 LNCS).
  • Dai, Y., Xue, B., & Zhang, M. (2014) "New Representations in PSO for Feature Construction in Classification". In Proceedings of the 16th European Conference on the Applications of Evolutionary Computation (EvoApplications 2014) (pp. 476-488). Berlin: Springer.
  • da Silva, A. S., Gao, X., & Andreae, P. (2014) "Wallace: Incorporating Search into Chatting". In Lecture Notes in Computer Science (pp. 842-848). Springer International Publishing. doi:10.1007/978-3-319-13560-1_68
  • Chen, G., Zhang, M., Pang, S., & Douch, C. (2014) "Stochastic Decision Making in Learning Classifier Systems through a Natural Policy Gradient Method". In C. K. Loo, K. S. Yap, K. W. Wong, A. Teoh, & K. Huang (Eds.), Neural Information Processing 21st International Conference, ICONIP 2014 Kuching, Malaysia, November 3–6, 2014 Proceedings, Part III Vol. 8836 (pp. 300-307). Berlin: Springer. doi:10.1007/978-3-319-12643-2_37
  • Bennett, S., Nguyen, S., & Zhang, M. (2014) "A Hybrid Discrete Particle Swarm Optimisation Method for Grid Computation Scheduling". In Proceedings of 2014 IEEE Congress on Evolutionary Computation (pp. 483-490). Beijing: IEEE Press.
  • Barddal, J. P., Gomes, H. M., & Enembreck, F. (2014) "SFNClassifier: A scale-free social network method to handle concept drift". In Proceedings of the ACM Symposium on Applied Computing (pp. 786-791). doi:10.1145/2554850.2554855
  • Arnold, D., Zhang, M., Urbanowicz, R., Iqbal, M., Shafi, K., Stonedahl, F., . . . Auger, A. (2014) "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion". Berlin: Springer.
  • Andreae, H., Andreae, P., Low, J., & Brown, D. (2014) "A Study Of Auti: A Socially Assistive Robotic Toy". In O. S. Iversen, P. Markopoulos, C. Dindler, F. Garzotto, & C. Frauenberger (Eds.), IDC ’14 - Proceedings of the 2014 Conference on Interaction Design and Children (pp. 245-248). New York: Association for Computing Machinery.
  • Alvarez, I. M., Browne, W. N., & Zhang, M. (2014) "Reusing learned functionality to address complex boolean functions". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8886, 383-394. doi:10.1007/978-3-319-13563-2_33
  • Alvarez, I. M., Browne, W. N., & Zhang, M. (2014) "Reusing learned functionality in XCS: Code fragments with constructed functionality and constructed features". In GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference (pp. 969-976). doi:10.1145/2598394.2611383
  • Alvarez, I., Browne, W., & Zhang, M. (2014) "On Learning the Hidden Multiplexer by Reusing Learned Functionality". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 383-394). Berlin: Springer.
  • Alvarez, I., Browne, W., & Zhang, M. (2014) "eusing learned functionality in XCS: code fragments with constructed functionality and constructed features". In Proceedings of 2014 Genetic and Evolutionary Computation Conference (GECCO 2014 Companion, IWLCS 2014) (pp. 969-976). Vancouver, Canada: ACM Press.
  • Al-Sahaf, H., Zhang, M., & Johnston, M. (2014) "Genetic Programming for Multiclass Texture Classification Using a Small Number of Instances". In Unknown Conference Vol. 8886 (pp. 335-346). Springer. doi:10.1007/978-3-319-13563-2_29
  • Al-Sahaf, H., Zhang, M., & Johnston, M. (2014) "Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification". In Proceedings of the 29th International Conference on Image and Vision Computing New Zealand (IVCNZ 2014) (pp. 84-89). ACM. doi:10.1145/2683405.2683418
  • Ahmed, S., Zhang, M., Peng, L., & Xue, B. (2014) "Multiple Feature Construction for Effective Biomarker Identification and Classification using Genetic Programming". In Proceedings of 2014 Genetic and Evolutionary Computation Conference (GECCO 2014) (pp. 249-256). Vancouver, Canada: ACM Press. doi:10.1145/2576768.2598292
  • Ahmed, S., Zhang, M., Peng, L., & Xue, B. (2014) "Genetic Programming for Measuring Peptide Detectability". In Proceedings of the 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (pp. 593-604). Berlin: Springer.
  • Ahmed, S., Zhang, M., & Peng, L. (2014) "Prediction of detectable peptides in ms data using genetic programming". In GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference (pp. 37-38). doi:10.1145/2598394.2598421
  • Ahmed, S., Zhang, M., & Peng, L. (2014) "Peptide Detection Prediction of MS data using Genetic Programming". In Proceedings of 2014 Genetic and Evolutionary Computation Conference (GECCO 2014 Companion) (pp. 37-38). Vancouver, Canada: ACM Press.
  • Ahmed, S., Zhang, M., & Peng, L. (2014) "Improving Feature Ranking for Biomarker Discovery in Proteomics Mass Spectrometry Data using Genetic Programming". Connection Science, 26(3), 215-243. doi:10.1080/09540091.2014.906388
  • Ahmed, S., Zhang, M., & Peng, L. (2014) "GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data". In Proceedings of the 16th European Conference on the Applications of Evolutionary Computation (EvoApplications 2014) (pp. 915-927). Berlin: Springer.
  • Ahmed, S., Zhang, M., & Peng, L. (2014) "A New GP-based Wrapper Feature Construction Approach to Classification and Biomarker Identification". In 2014 IEEE Congress on Evolutionary Computation (pp. 2756-2763). Beijing, China: IEEE Press.
  • Mirghasemi, S., Rayudu, R., & Zhang, M. (2013) "A new image segmentation algorithm based on modified seeded region growing and particle swarm optimization". In International Conference Image and Vision Computing New Zealand (pp. 382-387). Wellingtn, New Zealand. doi:10.1109/IVCNZ.2013.6727045
  • Marzukhi, S., Browne, W. N., & Zhang, M. (2013) "Adaptive artificial datasets through learning classifier systems for classification tasks". Evolutionary Intelligence, 6(2), 93-107. doi:10.1007/s12065-013-0094-y
  • Iqbal, M., Browne, W. N., & Zhang, M. (2013) "Learning complex, overlapping and niche imbalance Boolean problems using XCS-based classifier systems". Evolutionary Intelligence, 6(2), 73-91. doi:10.1007/s12065-013-0091-1
  • Gomes, H. M., & Enembreck, F. (2013) "SAE: Social Adaptive Ensemble classifier for data streams". In Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 (pp. 199-206). doi:10.1109/CIDM.2013.6597237
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2013) "A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem". IEEE Transactions on Evolutionary Computation, 17(5), 621-639. doi:10.1109/TEVC.2012.2227326
  • Crabtree, D., Gao, X., & Andreae, P. (2013) "Query directed clustering". Knowledge and Information Systems, 36(3), 693-729. doi:10.1007/s10115-012-0564-z
  • Marzukhi, S., Browne, W. N., & Zhang, M. (2013) "Adaptive artificial datasets to discover the effects of domain features for classification tasks". In GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (pp. 157-158). doi:10.1145/2464576.2464654
  • Marzukhi, S., Browne, W. N., & Zhang, M. (2013) "Adaptive artificial datasets through learning classifier systems for classification tasks". In GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (pp. 1243-1250). doi:10.1145/2464576.2466805
  • Xue, B., Cervante, L., Shang, L., Browne, W., & Zhang, M. (2013) "Multi-Objective Evolutionary Algorithms for Filter Based Feature Selection in Classification". International Journal on Artificial Intelligence Tools, 22(4), 1350024-1-1350024-31. doi:10.1142/S0218213013500243
  • Pang, S., Zhu, L., Chen, G., Sarrafzadeh, A., Ban, T., & Inoue, D. (2013) "Dynamic class imbalance learning for incremental LPSVM". Neural Networks, 44, 87-100. doi:10.1016/j.neunet.2013.02.007
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2013) "Hybrid evolutionary computation methods for quay crane scheduling problems". Computers & Operations Research, 40(8), 2083-2093. doi:10.1016/j.cor.2013.03.007
  • Ma, H., Noack, R., Schewe, K. D., Thalheim, B., & Wang, Q. (2013) "Complete conceptual schema algebras". Fundamenta Informaticae, 124(3), 271-295. doi:10.3233/FI-2013-834
  • Rada-Velila, J., Zhang, M., & Seah, K. (2013) "A performance study on synchronicity and neighborhood size in particle swarm optimization". Soft Computing, 17(6), 1019-1030. doi:10.1007/s00500-013-1015-9
  • Bhowan, U., Johnston, M., Zhang, M., & Yao, X. (2013) "Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data". IEEE Transactions on Evolutionary Computation, 17(3), 368-386. doi:10.1109/TEVC.2012.2199119
  • Xue, B., & Ma, W. D. (2013) "Optimization research of cavity milling processing parameters". In Proceedings - 2013 5th Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2013 (pp. 774-776). doi:10.1109/ICMTMA.2013.191
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2013) "Learning reusable initial solutions for multi-objective order acceptance and scheduling problems with genetic programming". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 7831 LNCS (pp. 157-168). doi:10.1007/978-3-642-37207-0_14
  • Cervante, L., Xue, B., Shang, L., & Zhang, M. (2013) "A multi-objective feature selection approach based on binary PSO and rough set theory". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 7832 LNCS (pp. 25-36). doi:10.1007/978-3-642-37198-1_3
  • Iqbal, M., Browne, W. N., & Zhang, M. (2013) "Evolving optimum populations with XCS classifier systems: XCS with code fragmented action". Soft Computing, 17(3), 503-518. doi:10.1007/s00500-012-0922-5
  • Iqbal, M., Browne, W., & Zhang, M. (2013) "Evolving optimum populations with XCS classifier systems: XCS with code fragmented action". Soft Computing, 17(3), 503-518. doi:10.1007/s00500-012-0922-5
  • Xie, H., & Zhang, M. (2013) "Parent Selection Pressure Auto-Tuning for Tournament Selection in Genetic Programming". IEEE Transactions on Evolutionary Computation, 17(1), 1-19. doi:10.1109/TEVC.2011.2182652
  • Zhang, M., Koeppen, M., & Damas, S. (2013) "Special Issue on Computational Intelligence in Computer Vision and Image Processing [Guest Editorial] (Vol". 8). IEEE: IEEE Computational Intelligence Magazine.
  • Yu, Y., Ma, H., & Zhang, M. (2013) "An Adaptive Genetic Programming Approach to QoS-aware Web Services Composition". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 1740-1747). IEEE Press.
  • Xue, B., Zhang, M., Dai, Y., & Browne, W. (2013) "PSO for Feature Construction and Binary Classification". In C. Blum (Ed.), GECCO'13 Proceedings of the 2013 Genetic and Evolutionary Computation Conference (pp. 137-144). New York: The Association for Computing Machinery, Inc. (ACM).. Retrieved from https://dl.acm.org/citation.
  • Xue, B., Zhang, M., & Browne, W. N. (2013) "Particle swarm optimization for feature selection in classification: A multi-objective approach". IEEE transactions on cybernetics, 43(6), 1656-1671. doi:10.1109/TSMCB.2012.2227469
  • Xue, B., Zhang, M., & Browne, W. N. (2013) "Novel Initialisation and Updating Mechanisms in PSO for Feature Selection in Classification". In Lecture Notes in Computer Science (pp. 428-438). Springer Berlin Heidelberg. doi:10.1007/978-3-642-37192-9_43
  • Xue, B., Zhang, M., & Browne, W. (2013) "Novel Initialisation and Updating Mechanisms in PSO for Feature Selection in Classification". In Proceedings of the 16th European Conference on Applications of Evolutionary Computation (EvoApplications 2013). Lecture Notes in Computer Science (Vol. 7835, pp. 428-438). Berlin: Springer.
  • Welch, I., Gao, X., Komisarczuk, P., & others. (2013) "Detecting heap-spray attacks in drive-by downloads: Giving attackers a hand". In 38th Annual IEEE Conference on Local Computer Networks (pp. 300-303). IEEE.
  • Wang, A., Ma, H., & Zhang, M. (2013) "Genetic Programming with Greedy Search for Web Service Composition". In Database and Expert Systems Applications (pp. 9-17). Springer-Verlag Berlin Heidelberg.
  • Wahid, A., Gao, X., & Andreae, P. (2013) "Exploiting User Queries for Search Result Clustering". In X. Lin, Y. Manolopoulos, D. Srivastava, & G. Huang (Eds.), Web Information Systems Engineering – WISE 2013 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part I Vol. 8180 (pp. 111-120). Nanjing, China: Springer. doi:10.1007/978-3-642-41230-1_10
  • Thompson, D., Bell, T., Andreae, P., & Robins, A. (2013) "The Role of Teachers in Implementing Curriculum Changes". In Proceeding of the 44th ACM Technical Symposium on Computer Science Education, SIGCSE '13 (pp. 245-250). New York: ACM.
  • Stojmirovic, A., Andreae, P., Boland, M., Jordan, T., Pestov, V., Brisaboa, N., . . . Zezula, P. (2013) "PFMFind: a system for discovery of peptide homology and function". In 6th International Conference, SISAP 2013 (pp. 319-324). doi:10.1007/978-3-642-41062-8_32
  • Stevanovic, A., Xue, B., & Zhang, M. (2013) "Feature Selection Based on PSO and Decision-Theoretic Rough Set Model". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 2840-2847). IEEE Press.
  • Song, L., Pang, S., Chen, G., Sarrafzadeh, H., Ban, T., & Inoue, D. (2013) "An incremental learning approach to continuous image change detection". In Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on (pp. 747-752). Shenyang, China. doi:10.1109/FSKD.2013.6816294
  • Setayeshbarhaghi, M., Zhang, M., & Johnston, M. (2013) "A Novel Particle Swarm Optimisation Approach to Detecting C ontinuous, Thin and Smooth Edges in Noisy Images". Information Sciences, 246, 28-51.
  • Setayeshbarhaghi, M., Johnston, M., & Zhang, M. (2013) "A Novel Particle Swarm Optimisation Approach to Detecting Continuous, Thin and Smooth Edges in Noisy Images". Information Sciences, 246, 28-51.
  • Scoble, A., Zhang, M., Browne, W., Bruce, Z., & Stephenson, B. (2013) "Evolutionary Spatial Auto-Correlation for Assessing Earthquake Liquefaction Potential using Parallel Linear Genetic Programming". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 2940-2947).
  • Samian, N., Seah, W., & Chen, A. (2013) "Quantifying Selfishness and Fairness in Wireless Multihop Networks". In Proceedings of the 38th Annual IEEE Conference on Local Computer Networks (pp. 1-9). Sydney: IEEE. doi:10.1109/LCN.2013.6761279
  • Roberts, R., Jones, T., & Lewis, J. (2013) "Synthesis of incidental detail as composable components in a functional language". In T. Rhee, R. Rayudu, C. Hollitt, J. Lewis, & M. Zhang (Eds.), Image and Vision Computing New Zealand (IVCNZ), 2013 28th International Conference of (pp. 305-310). Wellington, New Zealand.
  • Rada-Velila, J., Zhang, M., & Johnston, M. (2013) "Resampling in Particle Swarm Optimization". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 947-954).
  • Rada-Velila, J., Johnston, M., & Zhang, M. (2013) "Optimal Computing Budget Allocation in Particle Swarm Optimization". In Proceedings of 2013 Genetic and Evolutionary Computation Conference. (pp. 81-88).
  • Peng, Y., Pang, S., Chen, G., Sarrafzadeh, A., Ban, T., & Inoue, D. (2013) "Chunk incremental IDR/QR LDA learning". In International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Washington, DC: IEEE.
  • Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2013) "Dynamic multi-objective job shop scheduling: A genetic programming approach". Studies in Computational Intelligence, 505, 251-282. doi:10.1007/978-3-642-39304-4_10
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2013) "Learning Reusable Initial Populations for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming". In Proceedings of the 16th European Conference on Genetic Programming (EuroGP 2013). Lecture Notes in Computer Science (Vol. 7831, pp. 168). Berlin: Springer.
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2013) "Learning Iterative Dispatching Rules for Job Shop Scheduling with Genetic Programming". Special issue on Advanced Dispatching Rules for Large-scale Manufacturing Systems, International Journal of Advanced Manufacturing Technology, 67(1-4), 85-100.
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2013) "Hybrid evolutionary computation methods for quay crane scheduling problems Computers & Operations Research". (Journal of) Computers and Operations Research, 40(8), 2083-2093.
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2013) "Genetic Programming for Evolving Reusable Due-date Assignment Models in Job Shop Environment". Evolutionary Computation (Journal, MIT Press).
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2013) "A MO-GPHH Approach to Dynamic Job Shop Scheduling Problems". In N. Urquhart, S. Etaner-Uyar, & E. Ozcan (Eds.), Book Chapter on Automated Scheduling, Studies in Computational Intelligence (pp. 251-282). Springer.
  • Mirghasemi, S., Rayudu, R., & Zhang, M. (2013) "A New Image Segmentation Algorithm Based on Modified Seeded Region Growing and Particle Swarm Optimization". In Proceedings of 2013 the 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013) (pp. 282-387). IEEE Press.
  • Mirghasemi, S., Rayudu, R., & Zhang, M. (2013) "A feature-based region growing-merging approach to color image segmentation". In Proceedings of 2013 the 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013) (pp. 376-381). Wellington: IEEE Press. doi:10.1109/IVCNZ.2013.6727044
  • Mei, Y., Tang, K., & Yao, X. (2013) "Evolutionary Computation for Dynamic Capacitated Arc Routing Problem". In S. Yang, & X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems. Springer.
  • Mei, Y., Li, X., & Yao, X. (2013) "Decomposing large-scale capacitated arc routing problems using a random route grouping method". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 1013-1020). IEEE. doi:10.1109/CEC.2013.6557678
  • Male, J., Nguyen, S., Zhang, M., & Johnston, M. (2013) "Genetic Programming for Order Acceptance and Scheduling". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 1005-1012).
  • Male, J., Nguyen, S., Johnston, M., & Zhang, M. (2013) "Evolving Stochastic Dispatching Rules for Order Acceptance and Scheduling via Genetic Programming". In AI 2013: Advances in Artificial Intelligence, Lecture Notes in Computer Science (pp. 478-489).
  • Ma, H., & Schewe, K. D. (2013) "An algebra for geometric conceptual modelling". In 23rd European Japanese Conference on Information Modelling and Knowledge Bases (EJC 2013) (pp. 184-202).
  • Ma, H., Noack, R., Schewe, K. -D., Thalheim, B., & Wang, Q. (2013) "Complete Conceptual Schema Algebras". Fundamenta Informaticae, 123, 271-295. doi:10.3233/FI-2013-834
  • Ma, H., Looser, D., & Schewe, K. (2013) "Using Formal Concept Analysis for Ontology Design and Maintenance in Human Resource Recruitment". In 9th Asia-Pacific Conference on Conceptual Modelling (APCCM) Vol. 143 (pp. 61-68). CRPIT, Australian Computer Society.
  • Lewis, J., & Rhee, T. (2013) "Principal Component Analysis and Laplacian Splines: steps toward a unified model". In Proceedings of the Forum Math for Industry. Fukuoka: FMI.
  • Le, V. L., Welch, I., Gao, X., & Komisarczuk, P. (2013) "Anatomy of drive-by download attack". In Proceedings of the Eleventh Australasian Information Security Conference-Volume 138 (pp. 49-58).
  • Le, V., Welch, I., Gao, X., & Komisarczuk, P. (2013) "Anatomy of Drive-by Download Attack". In Australasian Information Security Conference (ACSW-AISC).
  • Lane, M., Xue, B., Liu, I., & Zhang, M. (2013) "Particle Swarm Optimisation and Statistical Clustering for Feature Selection". In AI2013.
  • Lane, M., Xue, B., Liu, I., & Zhang, M. (2013) "Particle Swarm Optimisation and Statistical Clustering for Feature Selection". In Proceedings of the 26th Australasian Joint Conference on Artificial Intelligence (AI2013). Lecture Notes in Computer Science (pp. 214-220). Berlin: Springer.
  • Klapaukh, R., Browne, W., & Zhang, M. (2013) "The Effect of Primitive Sets on the Expression of Evolved Images". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 725-732).
  • Jabeen, S., Gao, X., & Andreae, P. (2013) "Directional Context Helps: Guiding Semantic Relatedness Computation by Asymmetric Word Associations". In Lecture Notes in Computer Science (pp. 92-101). Springer Berlin Heidelberg. doi:10.1007/978-3-642-41230-1_8
  • Jabeen, S., Gao, X., & Andreae, P. (2013) "Directional Context Helps: Guiding Semantic Relatedness Computation by Asymmetric Word Associations". In X. Lin, Y. Manolopoulos, D. Srivastava, & G. Huang (Eds.), Web Information Systems Engineering – WISE 2013, 14th International Conference Nanjing, China, October 2013 Proceedings, Part I Vol. 8180 (pp. 92-101). Berlin Heidelberg: Springer. doi:10.1007/978-3-642-41230-1
  • Jabeen, S., Gao, X., & Andreae, P. (2013) "CPRel: Semantic Relatedness Computation Using Wikipedia based Context profiles". Journal of computing Science, April 2013..
  • Iqbal, M., Browne, W., & Zhang, M. (2013) "Learning Overlapping Natured and Niche Imbalance Boolean Problems Using XCS Classifier Systems". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 1818-1825). IEEE Press.
  • Iqbal, M., Browne, W., & Zhang, M. (2013) "Learning Complex, Overlapping and Niche Imbalance Boolean Problems Using XCS-Based Classifier Systems". Evolutionary Intelligence, 6(2), 71-91.
  • Iqbal, M., Browne, W., & Zhang, M. (2013) "Extending Scalable Learning Classifier System with Cyclic Graphs to Solve Complex, Large-Scale Boolean Problems". In Proceedings of 2013 Genetic and Evolutionary Computation Conference. (pp. 1045-1052).
  • Iqbal, M., Browne, W., & Zhang, M. (2013) "Comparison of Two Methods to Compute Action Value in XCS with Code-Fragment Action". In Proceedings of the Sixteenth International Workshop on Learning Classifier Systems (IWLCS 2013), GECCO (Companion) (pp. 1235-1242).
  • Ghifary, M., Kleijn, W., & Zhang, M. (2013) "Sparse Features in Deep Learning for Noise-Robust Digit Classification". In Proceedings of 2013 the 28th International Conference on Image and Vision Computing New Zealand (IVCNZ2013) (pp. 340-345). IEEE Press.
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Triangular-Distribution-Based Feature Construction Using Genetic Programming for Edge Detection". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 1732-1739).
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Low-level Feature Extraction for Edge Detection using Genetic Programming". IEEE Transactions on Cybernetics, 1-14.
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Investigation of Low-level Edge Feature Extraction using Three Blocks". In Proceedings of 2013 the 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013) (pp. 293-298). Wellington: IEEE Press.
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Genetic Programming for Edge Detection using Multivariate Density". In Proceedings of 2013 Genetic and Evolutionary Computation Conference. (pp. 917-924).
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Genetic Programming for Automatic Construction of Variant Features in Edge Detection". In Lecture Notes in Computer Science (pp. 354-364). Springer Berlin Heidelberg. doi:10.1007/978-3-642-37192-9_36
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Genetic Programming for Automatic Construction of Variant Features in Edge Detection". In Proceedings of the 16th European Conference on Applications of Evolutionary Computation (EvoApplications 2013). Lecture Notes in Computer Science (Vol. 7835, pp. 354-364). Berlin: Springer.
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Gaussian Mixture Models and Information Entropy for Image Segmentation using Particle Swarm Optimisation". In Proceedings of 2013 the 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013) (pp. 228-333). IEEE Press.
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Gaussian Mixture Models and Information Entropy for Image Segmentation using Particle Swarm Optimisation". In Proceedings of 2013 the 28th International Conference on Image and Vision Computing New Zealand (IVCNZ2013) (pp. 328-333). IEEE Press, Wellington: IEEE Press.
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Automatic Construction of Gaussian-Based Edge Detectors Using Genetic Programming". In Lecture Notes in Computer Science (pp. 365-375). Springer Berlin Heidelberg. doi:10.1007/978-3-642-37192-9_37
  • Fu, W., Johnston, M., & Zhang, M. (2013) "Automatic Construction of Gaussian-Based Edge Detectors Using Genetic Programming". In Proceedings of the 16th European Conference on Applications of Evolutionary Computation (EvoApplications 2013). Lecture Notes in Computer Science (Vol. 7835, pp. 365-375). Berlin: Springer.
  • Esparcia-Alcazar, A., Silva, S., Sim, K., & Zhang, M. (2013) "EvoApplications 2013: Proceedings of the 16th European Conference on Applications of Evolutionary Computation". Berlin: Springer (LNCS 7835).
  • Chen, G., Sarrafzadeh, A., & Pang, S. (2013) "Service provision control in federated service providing systems". IEEE Transactions on Parallel and Distributed Systems, 24(3), 587-600. doi:10.1109/TPDS.2012.150
  • Cervante, L., Xue, B., Shang, L., & Zhang, M. (2013) "Binary Particle Swarm Optimisation and Rough Set Theory for Dimension Reduction in Classification". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 2428-2435). IEEE Press.
  • Cervante, L., Xue, B., Shang, L., & Zhang, M. (2013) "A Multi-Objective Feature Selection Approach Based on Binary PSO and Rough Set Theory". In Proceedings of the 13th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2013). Lecture Notes in Computer Science (Vol. 7832, 25th ed ed., pp. 36). Berlin: Springer.
  • Cervante, L., & Gao, X. (2013) "Information and rough set theory based feature selection techniques". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8210 LNCS (pp. 166-176). doi:10.1007/978-3-319-02750-0_17
  • Bhowan, U., Zhang, M., & Johnston, M. (2013) "Comparing ensemble learning approaches in genetic programming for classification with unbalanced data". In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 135-136).
  • Bhowan, U., Johnston, M., Zhang, M., & Yao, X. (2013) "Reusing Genetic Programming for Ensemble Selection in Classification of Unbalanced Data". IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2013.2293393
  • Al-Sahaf, H., Zhang, M., & Johnston, M. (2013) "Binary image classification using genetic programming based on local binary patterns". In Proceedings of the 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013) (pp. 220-225). Springer. doi:10.1109/IVCNZ.2013.6727019
  • Al-Sahaf, H., Zhang, M., & Johnston, M. (2013) "A One-Shot Learning Approach to Image Classification Using Genetic Programming". In Proceedings of the 26th Australasian Joint Conference on Artificial Intelligence (AI 2013) (Vol. 8272, pp. 110-122). Springer. doi:10.1007/978-3-319-03680-9_13
  • Al-Sahaf, H., Zhang, M., & Johnston, M. (2013) "A One-shot Learning Approach to Image Classification using Genetic Programming". In S. Cranefield, & A. Nayak (Eds.), AI 2013: Advances in Artificial Intelligence 26th Australasian Joint Conference Dunedin, New Zealand, December 1-6, 2013 Proceedings (pp. 110-122). Berlin: Springer. doi:10.1007/978-3-319-03680-9
  • Al-Sahaf, H., Song, A., & Zhang, M. (2013) "Hybridisation of Genetic Programming and Nearest Neighbour for classification". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (CEC 2013) (pp. 2650-2657). IEEE. doi:10.1109/CEC.2013.6557889
  • Ahmed, S., Zhang, M., & Peng, L. (2013) "sEnhanced Feature Selection for Biomarker Discovery in LC-MS Data using GP". In 2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) (pp. 584-591). Retrieved from https://www.webofscience.
  • Ahmed, S., Zhang, M., & Peng, L. (2013) "Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach". In 11th European Conference, EvoBIO 2013 Vol. 7833 (pp. 43-55). Berlin: Springer.
  • Ahmed, S., Zhang, M., & Peng, L. (2013) "Enhanced Feature Selection for Biomarker Discovery in LC-MS Data using GP". In Proceedings of 2013 IEEE Congress on Evolutionary Computation (pp. 584-591). Cancun, Mexico. doi:10.1109/CEC.2013.6557621
  • Niu, B., Fan, Y., Xiao, H., & Xue, B. (2012) "Bacterial foraging based approaches to portfolio optimization with liquidity risk". Neurocomputing, 98, 90-100. doi:10.1016/j.neucom.2011.05.048
  • Ma, H., Schewe, K. D., Thalheim, B., & Wang, Q. (2012) "Conceptual Modelling of Services J". UCS special issue. Journal of Universal Computer Science, 18(17), 2361-2363.
  • Jabeen, S., Gao, X., & Andreae, P. (2012) "Using Wikipedia as an External Knowledge Source for Supporting Contextual Disambiguation". Journal of Software Engineering and Applications, 5(12B), 175-180. doi:10.4236/jsea.2012.512B034
  • Bell, T., Newton, H., Andreae, P., & Robins, A. (2012) "The introduction of computer Science to NZ high schools - An analysis of student work". In ACM International Conference Proceeding Series (pp. 5-15). doi:10.1145/2481449.2481454
  • Neshatian, K., Zhang, M., & Andreae, P. (2012) "A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming". IEEE Transactions on Evolutionary Computation, 16(5), 645-661. doi:10.1109/TEVC.2011.2166158
  • Ma, H., Schewe, K. -D., Thalheim, B., & Wang, Q. (2012) "A formal model for the interoperability of service clouds". Service-Oriented Computing and Applications, 6(3), 189-205. doi:10.1007/s11761-012-0101-7
  • Chandra, R., Frean, M., & Zhang, M. (2012) "Crossover-based local search in cooperative co-evolutionary feedforward neural networks". Applied Soft Computing, 12(9), 2924-2932. doi:10.1016/j.asoc.2012.04.010
  • Chandra, R., Frean, M., & Zhang, M. (2012) "On the issue of separability for problem decomposition in cooperative neuro-evolution". Neurocomputing, 87, 33-40. doi:10.1016/j.neucom.2012.02.005
  • Chandra, R., & Zhang, M. (2012) "Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction". Neurocomputing, 86, 116-123. doi:10.1016/j.neucom.2012.01.014
  • Chandra, R., Frean, M., & Zhang, M. (2012) "Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks". Soft Computing, 16(6), 1009-1020. doi:10.1007/s00500-011-0798-9
  • Zhu, L., Pang, S., Chen, G., & Sarrafzadeh, A. (2012) "Class imbalance robust incremental LPSVM for data streams learning". In Neural Networks (IJCNN), The 2012 International Joint Conference on (pp. 1-8). Brisbane, QLD, Australia: IEEE. doi:10.1109/IJCNN.2012.6252836
  • Xue, B., Zhang, M., & Browne, W. (2012) "Single feature ranking and binary Particle Swarm Optimisation based feature subset ranking for feature selection". In Proceedings of the Thirty-Fifth Australasian Computer Science Conference (ACSC 2012) (pp. 27-36). Melbourne: Australian Computer Society.
  • Xue, B., Zhang, M., & Browne, W. (2012) "Particle Swarm Optimisation for Feature Selection in Classification: A Multi-Objective Approach". IEEE Transactions on Systems, Man, and Cybernetics (Part B). doi:10.1109/TSMCB.2012.2227469
  • Xue, B., Zhang, M., & Browne, W. (2012) "New fitness functions in binary Particle Swarm Optimisation for feature selection". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (pp. 2145-2152). Washington, DC: IEEE Press.
  • Xue, B., Zhang, M., & Browne, W. (2012) "Multi-Objective Particle Swarm Optimisation (PSO) for Feature Selection". In T. Soule (Ed.), GECCO’12 Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation (pp. 81-88). New York: The Association for Computing Machinery, Inc. (ACM). Retrieved from https://dl.acm.org/citation.
  • Xue, B., Cervante, L., Shang, L., & Zhang, M. (2012) "A Particle Swarm Optimisation based multi-objective filter approach to feature selection for classification". In Proceedings of the 12th Pacific Rim International Conference on Artificial Intelligence (pp. 673-685). Kuching, Sarawak: PRICAI.
  • Xue, B., Cervante, L., Shang, L., Browne, W., & Zhang, M. (2012) "A multi-objective particle swarm optimisation for filter-based feature selection in classification problems". Connection Science, 24(2-3), 91-116. doi:10.1080/09540091.2012.737765
  • Welch, I., Gao, X., Komisarczuk, P., & others. (2012) "A novel scoring model to detect potential malicious web pages". In 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (pp. 254-263). IEEE.
  • Wang, D., Gao, X., & Andreae, P. (2012) "DIKEA: Domain-Independent Keyphrase Extraction Algorithm". In Australasian Conference on Artificial Intelligence (pp. 719-730). Berlin: Springer.
  • Wang, D., Gao, X., & Andreae, P. (2012) "Automatic Keyword Extraction from Single-Sentence Natural Language Queries". In PRICAI (pp. 637-648). Berlin: Springer.
  • Song, A., & Zhang, M. (2012) "Genetic Programming for Detecting Target Motions". Connection Science, 24(2-3), 117-141. doi:10.1080/09540091.2012.744873
  • Shang, L., Wang, H., Dai, X., & Zhang, M. (2012) "Opinion targets extraction for short comments". In Proceeding of the 12th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2012) (pp. 528-539). Berlin: Springer.
  • Setayeshbarhaghi, M., Zhang, M., & Johnston, M. (2012) "Effects of Static and Dynamic Topologies in Particle Swarm Optimisation for Edge Detection in Noisy Images". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (pp. 8-15). Washington, DC: IEEE Press.
  • Setayeshbarhaghi, M., Zhang, M., & Johnston, M. (2012) "A Spatial Random-Meaningful Neighbourhood Topology in PSO for Edge Detection in Noisy Images". In Proceedings of 2012 Genetic and Evolutionary Computation Conference (GECCO (Companion) 2012) (pp. 1403-1404). Philadelphia, USA: ACM Press.
  • Scoble, A., Johnston, M., & Zhang, M. (2012) "Local Search in Parallel Linear Genetic Programming for Multiclass Classification". In Proceedings of the 25th Australasian Joint Conference on Artificial Intelligence (pp. 373-384). Sydney, Australia: Springer.
  • Rada-Velila, J., Zhang, M., & Seah, K. (2012) "Evaporation mechanisms for Particle Swarm Optimization". In Proceedings of the Ninth International Conference on Simulated Evolution And Learning (SEAL2012) (pp. 238-247). Berlin: Springer.
  • Rada Vilela, J., Zhang, M., & Seah, K. (2012) "A performance study on the effects of noise and evaporation in Particle Swarm Optimization". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (pp. 873-880). Washington, DC: IEEE Press.
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2012) "Evolving Reusable Operation-Based Due-Date Assignment Models for Job Shop Scheduling with Genetic Programming". In Proceedings of the 15th European Conference on Genetic Programming (EuroGP) (pp. 121-133). Berlin: Springer. doi:10.1007/978-3-642-29139-5_11
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2012) "Automatic Discovery of Optimisation Search Heuristics for Two Dimensional Strip Packing using Genetic Programming". In Proceedings of the Ninth International Conference on Simulated Evolution and Learning (SEAL2012) (pp. 341-350). Hanoi, Vietnam: Springer.
  • Nguyen, S., Zhang, M., Johnston, M., & Chen Tan, K. (2012) "A Coevolution Genetic Programming Method to Evolve Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling Problems". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (pp. 3261-3268). Washington, DC: IEEE Press.
  • Neshatian, K., & Zhang, M. (2012) "Improving relevance measures using Genetic Programming". In Proceedings of the 15th European Conference on Genetic Programming (pp. 97-108). Berlin: Springer. doi:10.1007/978-3-642-29139-5_9
  • Marzukhi, S., Browne, W., & Zhang, M. (2012) "Two-cornered learning classifier systems for pattern generation and classification". In Proceedings of 2012 Genetic and Evolutionary Computation Conference (GECCO 2012) (pp. 895-902). New York: ACM Press.
  • Male, J., Gao, X., & Andreae, P. (2012) "Query Directed Web Page Clustering using Suffix Tree and Wikipedia Links". In International Conference on Advanced Data Mining and Applications (pp. 91-99). Berlin: Springer.
  • Ma, H., Schewe, K. -D., Thalheim, B., & Wang, Q. (2012) "Conceptual Modelling of Services". JOURNAL OF UNIVERSAL COMPUTER SCIENCE, 18(17), 2361-2363. Retrieved from https://www.webofscience.
  • Ma, H. (2012) "Transforming Geometrically Enhanced Conceptual Model Schemas to GML". In Lecture Notes in Computer Science (pp. 251-267). Springer Berlin Heidelberg. doi:10.1007/978-3-642-28279-9_18
  • Ma, H. (2012) "Transforming Geometrically Enhanced Conceptual Model Schemas to GML". In A. Düsterhöft, K. -D. Schewe, & M. Klettke (Eds.), Conceptual Modelling and Its Theoretical Foundations: Essays Dedicated to Bernhard Thalheim on the Occasion of His 60th Birthday (Vol. 7260, pp. 251-267). Heidelberg Dordrecht London New York: Springer. doi:10.1007/978-3-642-28279-9
  • Li, R., Li, W., Shang, L., Gao, Y., & Zhang, M. (2012) "Opponent's style modeling based on situations for Bayesian Poker". In Proceedings of the 25th Australasian Joint Conference on Artificial Intelligence (pp. 385-396). Berlin: Springer.
  • Jabeen, S., Gao, X., & Andreae, P. (2012) "Harnessing Wikipedia Semantics for Computing Contextual Relatedness". In PRICAI (pp. 861-865). Berlin: Springer.
  • Iqbal, M., Browne, W., & Zhang, M. (2012) "XCSR with computed continuous action". In Proceedings of the 25th Australasian Joint Conference on Artificial Intelligence (pp. 350-361). Berlin: Springer.
  • Iqbal, M., Browne, W., & Zhang, M. (2012) "Extracting and using building blocks of knowledge in learning classifier systems". In Proceedings of 2012 Genetic and Evolutionary Computation Conference (GECCO 2012) (pp. 863-870). New York: ACM Press.
  • Hunt, R., Neshatian, K., & Zhang, M. (2012) "Scalability analysis of Genetic Programming classifiers". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (pp. 1416-1422). Washington, DC: IEEE Press.
  • Hunt, R., Neshatian, K., & Zhang, M. (2012) "A Genetic Programming approach to hyper-heuristic feature selection". In Proceedings of the Ninth International Conference on Simulated Evolution And Learning (SEAL2012) (pp. 320-330). Berlin: Springer.
  • Huayang, X., & Zhang, M. (2012) "Parent Selection Pressure Auto-tuning for Tournament Selection in Genetic Programming". In IEEE Transactions on Evolutionary Computation. doi:10.1109/TEVC.2011.2182652
  • Huayang, X., & Zhang, M. (2012) "Impacts of sampling strategies in tournament selection for genetic programming". Journal of Soft Computing, 16(4), 615-633.
  • Hindmarsh, S., Andreae, P., & Zhang, M. (2012) "Genetic Programming for improving image descriptors generated using the Scale-Invariant Feature Transform". In Proceedings of the 27th International Conference on Image and Vision Computing New Zealand (pp. 85-90). New York: ACM Press.
  • Fu, W., Johnston, M., & Zhang, M. (2012) "Soft Edge Maps From Edge Detectors Evolved by Genetic Programming". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (pp. 24-31). Washington, DC: IEEE Press.
  • Fu, W., Johnston, M., & Zhang, M. (2012) "Multi-Frequency Transformation for Edge Detection". In Proceedings of the 27th International Conference on Image and Vision Computing New Zealand (pp. 204-209). New York: ACM Press. doi:10.1145/2425836.2425879
  • Fu, W., Johnston, M., & Zhang, M. (2012) "Genetic Programming for Edge Detection via Balancing Individual Training Images". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (pp. 2597-2604). Washington, DC: IEEE Press.
  • Fu, W., Johnston, M., & Zhang, M. (2012) "Genetic Programming for Edge Detection Using Blocks to Extract Features". In Proceedings of 2012 Genetic and Evolutionary Computation Conference (GECCO 2012) (pp. 855-862). Philadelphia, USA: ACM Press.
  • Fu, W., Johnston, M., & Zhang, M. (2012) "Genetic Programming for Edge Detection Based on Figure of Merit". In Proceedings of 2012 Genetic and Evolutionary Computation Conference (GECCO (Companion) 2012) (pp. 1483-1484). Philadelphia, USA: ACM Press.
  • Fu, W., Johnston, M., & Zhang, M. (2012) "Figure of Merit Based Fitness Functions in Genetic Programming For Edge Detection". In Proceedings of the Ninth International Conference on Simulated Evolution And Learning (SEAL2012) (pp. 22-31). Hanoi, Vietnam: Springer.
  • Fu, W., Johnston, M., & Zhang, M. (2012) "Automatic Construction of Invariant Features Using Genetic Programming for Edge Detection". In Proceedings of the 25th Australasian Joint Conference on Artificial Intelligence (pp. 144-155). Sydney, Australia: Springer.
  • Ebadi, T., Browne, W., & Zhang, M. (2012) "XCS-based versus UCS-based feature pattern classification system". In Proceedings of 2012 Genetic and Evolutionary Computation Conference (GECCO 2012) (pp. 839-846). New York: ACM Press.
  • Downey, C., Zhang, M., & Liu, J. (2012) "Parallel Linear Genetic Programming for Multi-class Classification". Genetic Programming and Evolvable Machines, 13(3), 275-304. doi:10.1007/s10710-012-9162-9
  • Chen, G., Pang, S., Sarrafzadeh, A., Ban, T., & Inoue, D. (2012) "SDE-Driven service provision control". In International Conference on Neural Information Processing (pp. 260-268). Doha, Qatar: Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-34475-6_32
  • Chandra, R., Zhang, M., & Peng, L. (2012) "Application of cooperative convolution optimization for 13C Metabolic Flux Analysis: Simulation of isotopic labelling patterns based on tandem mass spectrometry measurements". In Proceedings of the Ninth International Conference on Simulated Evolution and Learning (SEAL2012) (pp. 178-187). Berlin: Springer.
  • Chandra, R., Frean, M., & Zhang, M. (2012) "Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks". Soft Computing, 16, 1009-1020.
  • Cervante, L., Xue, B., Zhang, M., & Shang, L. (2012) "Binary Particle Swarm Optimisation for feature selection: a filter based approach". In WCCI 2012 IEEE World Congress on Computational Intelligence (pp. 8 pages). Washington, DC: IEEE. doi:10.1109/CEC.2012.6256452
  • Cervante, L., Xue, B., Shang, L., & Zhang, M. (2012) "A dimension reduction approach to classification based on Particle Swarm Optimisation and Rough Set Theory". In Proceedings of the 25th Australasian Joint Conference on Artificial Intelligence (pp. 313-325). Berlin: Springer.
  • Carnegie, D., Watterson, C., Andreae, P., & Browne, W. (2012) "Prediction of Success in Engineering Study". In Proceedings of the IEEE Engineering Education Conference (Educon 2012) (pp. 57-65).
  • Carnegie, D., Watterson, C., Andreae, P., & Browne, W. (2012) "NCEA as a predictor of success in tertiary engineering study". In Proceedings of the 19th Electronics New Zealand Conference (ENZCon) (pp. 7-12).
  • Bruce, C., Gao, X., Andreae, P., & Jabeen, S. (2012) "Query Expansion Powered by Wikipedia Hyperlinks". In Australasian Conference on Artificial Intelligence (pp. 421-432). Berlin: Springer.
  • Bhowan, U., Johnston, M., & Zhang, M. (2012) "Developing New Fitness Functions in Genetic Programming for Classification with Unbalanced Data". IEEE Transactions on Systems, Man, and Cybernetics (Part B)., 42(2), 406-421. doi:10.1109/TSMCB.2011.2167144
  • Bhowan, U., Johnston, M., & Zhang, M. (2012) "Developing in Genetic Programming for Classification with Unbalanced Data". In IEEE Transactions on Systems, Man, and Cybernetics (Part B) Vol. 42 (pp. 406-421). doi:10.1109/TSMCB.2011.2167144
  • Bhowan, U. (2012) "Genetic programming for classification with unbalanced data". (PhD Thesis).
  • Bell, T., Andreae, P., & Robins, A. (2012) "Computer science in NZ high schools: the first year of the new standards". In SIGCSE '12 Proceedings of the 43rd ACM technical symposium on Computer Science Education (pp. 343-348). New York: ACM. doi:10.1145/2157136.2157240
  • Al-Sahaf, H., Song, A., Neshatian, K., & Zhang, M. (2012) "Two-tier Genetic Programming: Towards Raw Pixel-based Image Classification". Expert Systems with Applications, 39, 12291-12301. doi:10.1016/j.eswa.2012.02.123
  • Al-Sahaf, H., Song, A., Neshatian, K., & Zhang, M. (2012) "Extracting Image Features for Classification by Two-tier Genetic Programming". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (CEC 2012) (pp. 567-574). IEEE. doi:10.1109/CEC.2012.6256412
  • Ahmed, S., Zhang, M., & Peng, L. (2012) "Genetic Programming for biomarker detection in mass spectrometry data". In Proceedings of the 25th Australasian Joint Conference on Artificial Intelligence (pp. 266-278). Berlin: Springer.
  • Abdulhamid, F., Song, A., Neshatian, K., & Zhang, M. (2012) "Evolving Genetic Programming classifiers with loop structures". In Proceedings of 2012 IEEE Congress on Evolutionary Computation (pp. 2581-2588). Washington, DC: IEEE Press.
  • Chen, G., Baghaei, N., Sarrafzadeh, A., Manford, C., Marshall, S., & Court, G. (2011) "Designing games to educate diabetic children". In Proceedings of the 23rd Australian Computer-Human Interaction Conference, OzCHI 2011 (pp. 72-75). doi:10.1145/2071536.2071546
  • Wei, J., & Zhang, M. (2011) "A memetic particle swarm optimization for constrained multi-objective optimization problems". In 2011 IEEE Congress of Evolutionary Computation, CEC 2011 (pp. 1636-1643). doi:10.1109/CEC.2011.5949811
  • Zhang, M. (2011) "Experience of teaching computational intelligence in an undergraduate level course". IEEE Computational Intelligence Magazine, 6(3), 57-59. doi:10.1109/MCI.2011.941591
  • Zhang, M., Kirley, M., & Li, X. (2011) "Special issue on evolutionary optimisation and learning". Soft Computing, 15(9), 1671-1673. doi:10.1007/s00500-010-0609-8
  • Xie, H., & Zhang, M. (2011) "Depth-Control Strategies for Crossover in Tree-based Genetic Programming". Soft Computing, 15(9), 1865-1878. doi:10.1007/s00500-011-0700-9
  • Worawitphinyo, P., Gao, X., & Jabeen, S. (2011) "Improving Suffix Tree Clustering with New Ranking and Similarity Measures J". Tang et al.(Eds.). In ADMA 2011, Part II, LNAI 7121 (pp. 55-68).
  • Welch, I., Gao, X., Komisarczuk, P., & others. (2011) "Two-stage classification model to detect malicious web pages". In 2011 IEEE International Conference on Advanced Information Networking and Applications (pp. 113-120). IEEE.
  • Wei, J., & Zhang, M. (2011) "Simplex Model Based Evolutionary Algorithm for Dynamic Multi-Objective Optimization". In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence (pp. 372-381). Berlin: Springer.
  • Wei, J., & Zhang, M. (2011) "Attraction Based PSO with Sphere Search for Dynamic Constrained Multi-Objective Optimisation Problem". In 13th Annual Genetic and Evolutionary Computation Conference (pp. 77-78). ACM New York, USA. doi:10.1145/2001858.2001904
  • Wei, J., & Zhang, M. (2011) "A Memetic Particle Swarm Optimization for Constrained Multi-objective Optimization Problems". In IEEE Congress on Evolutionary Computation (pp. 1635-1643).
  • Wah, E., Mei, Y., & Wah, B. W. (2011) "Portfolio optimization through data conditioning and aggregation". In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 253-260). IEEE. doi:10.1109/ICTAI.2011.46
  • Setayeshbarhaghi, M., Zhang, M., & Johnston, M. (2011) "Investigating Particle Swarm Optimisation Topologies for Edge Detection in Noisy Images". In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence, LNAI Vol. 7106 (pp. 609-618). Springer.
  • Setayeshbarhaghi, M., Zhang, M., & Johnston, M. (2011) "Edge Detection Using Constrained Discrete Particle Swarm Optimisation in Noisy Images". In IEEE Congress on Evolutionary Computation (pp. 246-253).
  • Setayeshbarhaghi, M., Zhang, M., & Johnston, M. (2011) "Detection of Continuous Smooth and Thin Edges in Noisy Images Using Constrained Particle Swarm Optimisation". In N. Krasnogor, & E. al (Eds.), 13th Annual Genetic and Evolutionary Computation Conference (pp. 45-52). New York: ACM. doi:10.1145/2001576.2001584
  • Setayeshbarhaghi, M., Johnston, M., & Zhang, M. (2011) "A Novel Local Thresholding Technique in PSO for Detecting Continuous Edges in Noisy Images". In Proceedings of the Twenty-Sixth International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 333-339). Auckland.
  • Scoble, A., Johnston, M., & Zhang, M. (2011) "Eliminating Useless Object Detectors Evolved in Multiple-Objective Genetic Programming". In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence, LNAI Vol. 7106 (pp. 341-350). Springer.
  • Rada Vilela, J., Zhang, M., & Seah, K. (2011) "Random Asynchronous PSO". In Proceedings of the 5th International Conference on Automation, Robots and Applications (ICARA 2011). Wellington: IEEE Press, December 6-8.
  • Rada Vilela, J., Zhang, M., & Seah, K. (2011) "A performance study on synchronous and asynchronous updates in particle swarm optimization". In N. Krasnogor, & E. al (Eds.), 13th Annual Genetic and Evolutionary Computation Conference (pp. 21-28). New York: ACM. doi:10.1145/2001576.2001581
  • Nguyen, S., Zhang, M., & Johnston, M. (2011) "A Genetic Programming Based Hyper-heuristic Approach for Combinatorial Optimisation". In N. Krasnogor, & E. al (Eds.), 13th Annual Genetic and Evolutionary Computation Conference (pp. 1299-1306). New York, USA: ACM. doi:10.1145/2001576.2001752
  • Nguyen, G., Gao, X., & Andreae, P. (2011) "Phoneme based representation for Vietnamese Web Page classification". In Proceedings of the 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Vol. August (pp. 15-22). Lyon, France. doi:10.1109/WI-IAT.2011.142
  • Neshatian, K., & Zhang, M. (2011) "Using genetic programming for context-sensitive feature scoring in classification problems". Connection Science, 23(3), 183-207. doi:10.1080/09540091.2011.630065
  • Mohemmed, A., Johnston, M., & Zhang, M. (2011) "Particle swarm optimisation based AdaBoost for object detection". Soft Computing - A Fusion of Foundations, Methodologies and Applications, 15(9), 1793-1805. doi:10.1007/s00500-010-0615-x
  • Mei, Y., Tang, K., & Yao, X. (2011) "Decomposition-based memetic algorithm for multiobjective capacitated arc routing problem". IEEE Transactions on Evolutionary Computation, 15, 151-165. doi:10.1109/TEVC.2010.2051446
  • Mei, Y., Tang, K., & Yao, X. (2011) "A memetic algorithm for periodic capacitated arc routing problem". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41, 1654-1667. doi:10.1109/TSMCB.2011.2158307
  • Marzukhi, S., Browne, W., & Zhang, M. (2011) "Developing an Evolvable Pattern Generator Using Learning Classifier Systems". In International Conference on Automation, Robotics and Applications (pp. 163-168). IEEE: New York.
  • Ma, H., Schewe, K. D., Thalheim, B., & Wang, Q. (2011) "Cloud Warehousing". Journal of Universal Computer Science, 17(8), 1183-1201.
  • Ma, H., & Schewe, K. -D. (2011) "Conceptual Geometric Modelling". In Handbook of Conceptual Modeling (pp. 421-440). Springer Berlin Heidelberg. doi:10.1007/978-3-642-15865-0_13
  • Ma, H., & Schewe, K. D. (2011) "Conceptual Geometric Modelling". In Handbook of Conceptual Modelling (pp. 331-349). Springer.
  • Ma, H. (2011) "A Geometrically Enhanced Conceptual Model and Query Language". Journal of Universal Computer Science, 16(20), 2986-3015.
  • Le, V. L., Welch, I., Gao, X. S., & Komisarczuk, P. (2011) "Identification of potential malicious web pages". __.
  • Kukenys, I., Browne, W., & Zhang, M. (2011) "Transparent, Online Image Pattern Classification Using a Learning Classier System". In Proceedings of the 13th European event on evolutionary computation in image analysis and signal processing (pp. 183-193). Torino, Italy: Springer. Retrieved from http://www.springerlink.
  • Kukenys, I., Browne, W., & Zhang, M. (2011) "Confusion matrices for improving performance of feature pattern classifier systems". In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 181-182). doi:10.1145/2001858.2001959
  • Iqbal, M., Browne, W., & Zhang, M. (2011) "Automatically defined functions for learning classifier systems". In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 375-382). Companion. doi:10.1145/2001858.2002022
  • Hunt, R., Johnston, M., & Zhang, M. (2011) "Improving Robustness in Multi-Objective Genetic Programming for Object Detection". In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence, LNAI Vol. 7106 (pp. 311-320). Springer.
  • Fu, W., Johnston, M., & Zhang, M. (2011) "Hybrid Particle Swarm Optimisation Based on History Information Sharing". In K. Natalio (Ed.), 13th Annual Genetic and Evolutionary Computation Conference (pp. 77-84). New York, USA: ACM. doi:10.1145/2001576.2001588
  • Fu, W., Johnston, M., & Zhang, M. (2011) "Genetic Programming For Edge Detection: A Global Approach". In IEEE Congress on Evolutionary Computation (pp. 254-261).
  • Fu, W., Johnston, M., & Zhang, M. (2011) "Genetic Programming for Edge Detection Based on Accuracy of Each Training Image". In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence, LNAI (pp. 301-310). Springer.
  • Fu, W., Johnston, M., & Zhang, M. (2011) "Analysis of Diagonal Derivatives in Edge Detectors Evolved by Genetic Programming". In Proceedings of the Twenty-Sixth International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 345-350). Auckland.
  • Fu, W., Johnston, M., & Zhang, M. (2011) "A Hybrid Particle Swarm Optimisation with Differential Evolution Approach to Image Segmentation". In Proceedings of the 13th European Workshop on evolutionary computation in image analysis and signal processing (EvoApplications 2011) Vol. 6624 (pp. 173-182). Torino, Italy: Springer. Retrieved from http://www.springerlink.
  • Friedlander, A., Neshatian, K., & Zhang, M. (2011) "Meta-Learning and Feature Ranking Using Genetic Programming for Classification: Variable Terminal Weighting". In IEEE Congress on Evolutionary Computation (pp. 941-948). New Orleans, United States of America: IEEE Press.
  • Downey, C., & Zhang, M. (2011) "Parallel Linear Genetic Programming". In Proceedings of the 14th European Conference on Genetic Programming Vol. 6621 (pp. 178-189). Torino, Itay: Springer. Retrieved from http://www.springerlink.
  • Downey, C., & Zhang, M. (2011) "Execution Trace Caching for Linear Genetic Programming". In IEEE Congress on Evolutionary Computation (pp. 1186-1193). New Orleans, United States of America: IEEE Press.
  • Downey, C., & Zhang, M. (2011) "Caching for Parallel Linear Genetic Programming". In 13th Annual Genetic and Evolutionary Computation Conference (pp. 201-202).
  • Chandra, R., Frean, M., Zhang, M., & Omlin, C. W. (2011) "Encoding subcomponents in cooperative co-evolutionary recurrent neural networks". Neurocomputing, 74, 3223-3234.
  • Chandra, R., Frean, M., & Zhang, M. (2011) "Modularity adaptation in cooperative coevolution of feedforward neural networks". In Neural Networks (IJCNN), The 2011 International Joint Conference on (pp. 681-688). IEEE.
  • Chan, A., Andreae, P., Northcote, P., & Miller, J. (2011) "Peloruside A inhibits microtubule dynamics in MCF7 breast cancer cells". In 25th Conference of the Microscopy Society of New Zealand. Wellington, New Zealand.
  • Chan, A., Andreae, P., Northcote, P., & Miller, J. (2011) "Peloruside A inhibits microtubule dynamics in a breast cancer cell line MCF7". Invest New Drugs, 29, 615-626. doi:10.1007/s10637-010-9398-2
  • Cassell, K., Anslow, C., Groves, L., & Andreae, P. (2011) "Visualizing the refactoring of classes via clustering". In Proceedings of the Thirty-Fourth Australasian Computer Science Conference-Volume 113 (pp. 63-72). Australian Computer Society, Inc..
  • Cassell, K., Andreae, P., & Groves, L. (2011) "A Dual Clustering Approach to the Extract Class Refactoring". In Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering (pp. 77-82). Skokie, IL: Knowledge Systems Institute Graduate School. Retrieved from http://dblp.uni-trier.
  • Bhowan, U., Zhang, M., & Johnston, M. (2011) "Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data". In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence (pp. 192-202). Berlin: Springer.
  • Bhowan, U., Johnston, M., & Zhang, M. (2011) "Evolving Ensembles in Multi-objective Genetic Programming for Classification with Unbalanced Data". In N. Krasnogor, & E. al (Eds.), Proceedings of 13th Annual Genetic and Evolutionary Computation Conference (pp. 1331-1338). Dublin: ACM. doi:10.1145/2001576.2001756
  • Atkins, D., Neshatian, K., & Zhang, M. (2011) "A domain independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification". In Proceedings of the 2011 IEEE Congress on Evolutionary Computation (pp. 238-245). New Orleans: IEEE Press.
  • Al-Sahaf, H., Neshatian, K., & Zhang, M. (2011) "Two-tier genetic programming for automatic feature extraction, feature selection and image classification". In Proceedings of the 26th International Conference on Image and Vision Computing New Zealand (IVCNZ 2011) (pp. 109-114). IEEE.
  • Al-Sahaf, H., Neshatian, K., & Zhang, M. (2011) "Automatic feature extraction and image classification using genetic programming". In Proceedings of the 5th International Conference on Automation, Robots and Applications (ICARA 2011) (pp. 157-162). IEEE Press. doi:10.1109/ICARA.2011.6144874
  • Abdulhamid, F., Neshatian, K., & Zhang, M. (2011) "Image Recognition using Genetic Programming with Loop Structures". In Proceedings of the Twenty-sixth International Conference on Image and Vision Computing New Zealand (pp. 553-558). Auckland.
  • Abdulhamid, F., Neshatian, K., & Zhang, M. (2011) "Genetic Programming for Evolving Programs with Loop Structures for Classification Tasks". In Proceedings of the 5th International Conference on Automation, Robots and Applications (ICARA 2011) (pp. 202-207). Wellington: IEEE Press, December 6-8.
  • Niu, B., Tan, L., Xue, B., Li, L., & Chai, Y. (2010) "Constrained portfolio selection using multiple swarms". In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. doi:10.1109/CEC.2010.5585943
  • Ma, H., Schewe, K. D., & Xie, H. (2010) "Using XML for cloud specification and XQuery for service discovery". In iiWAS2010 - 12th International Conference on Information Integration and Web-Based Applications and Services (pp. 126-133). doi:10.1145/1967486.1967509
  • Bell, T., Andreae, P., & Lambert, L. (2010) "Computer Science in New Zealand High Schools". Conferences in Research and Practice in Information Technology Series, 103, 15-22.
  • Chen, G., Sarrafzadeh, A., Low, C. P., & Zhang, L. (2010) "A self-organization mechanism based on cross-entropy method for P2P-like applications". ACM Transactions on Autonomous and Adaptive Systems, 5(4). doi:10.1145/1867713.1867716
  • Li, L., Xue, B., Tan, L., & Niu, B. (2010) "Improved particle swarm optimizers with application on constrained portfolio selection". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 6215 LNCS (pp. 579-586). doi:10.1007/978-3-642-14922-1_72
  • Mohemmed, A. W., Zhang, M., & Browne, W. N. (2010) "Particle swarm optimisation for outlier detection". In Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 (pp. 833-834). doi:10.1145/1830483.1830498
  • Niu, B., Fan, Y., Zhao, P., Xue, B., Li, L., & Chai, Y. (2010) "A novel bacterial foraging optimizer with linear decreasing chemotaxis step". In Proceedings - 2010 2nd International Workshop on Intelligent Systems and Applications, ISA 2010. doi:10.1109/IWISA.2010.5473527
  • Song, A., Chen, D., & Zhang, M. (2010) "Contribution Based Bloat Control in Genetic Programming". In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1725-1732). Washington, DC: IEEE.
  • Setayeshbarhaghi, M., Zhang, M., & Johnston, M. (2010) "Improving Edge Detection Using Particle Swarm Optimisation". In Proceedings of the 25th International Conference of Image and Vision Computing. Queenstown: ICVNZ.
  • Setayeshbarhaghi, M., Johnston, M., & Zhang, M. (2010) "Edge and Corner Extraction Using Particle Swarm Optimisation". In J. Li (Ed.), Advances in Artificial Intelligence (Vol. 6464, pp. 323-333). Berlin: Springer-Verlag.
  • Robertson, G., & Gao, X. (2010) "Improving AbraQ: An Automatic Query Expansion Algorithm". In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Vol. 1 (pp. 653-656). Washington, DC: IEEE.
  • Mohemmed, A., Zhang, M., & Browne, W. (2010) "Particle Swarm Optimisation for Outlier Detection". In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 83-84). Portland: ACM.
  • Mohemmed, A., Johnston, M., & Zhang, M. (2010) "Particle swarm optimisation based AdaBoost for object detection". A fusion of Foundations, Methodologies and Applications.
  • Mei, Y., Tang, K., & Yao, X. (2010) "Capacitated arc routing problem in uncertain environments". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. doi:10.1109/CEC.2010.5586031
  • Macri, L., Browne, W., & Zhang, M. (2010) "Particle Swarm Optimisation Based AdaBoost for Facial Expression Classication of Still Images". In Proceedings of the 25th International Conference of Image and Vision Computing NZ. Queenstown: IVCNZ.
  • Ma, H., Schewe, K. D., & Xie, H. (2010) "Using XML for Cloud Specification and XQuery for Service Discovery". In Information Integration and Web-based Applications & Services (iiWAS) (pp. 124-131). New York: ACM.
  • Ma, H., & Schewe, K. D. (2010) "Fragmentation of XML Documents Revisited". Information and Data Management, 1(1), 1-36.
  • Ma, H., & Schewe, K. D. (2010) "Fragmentation of XML Documents". Journal of Information and Data Management, 1(1), 21-33.
  • Ma, H., Noack, R., Schewe, K. D., & Thalheim, B. (2010) "Using Meta-Structures in Database Design". Informatica, 34, 387-403.
  • Ma, H. (2010) "Ontology-based Agri-Environmental Planning for Whole Farm Plans". In Conceptual Modeling (ER) Workshops, Lecture Notes in Computer Science Vol. 6413 (pp. 65-74). Berlin: Springer.
  • Liddle, T., Johnston, M., & Zhang, M. (2010) "Multi-Objective Genetic Programming for Object Detection". In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 3345-3352). Washington, DC: IEEE.
  • Larris, J., Zhang, M., & Browne, W. (2010) "Using Unrestricted Loops in Genetic Programming for Image Classification". In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1-8). Washington, DC: IEEE.
  • Kinzett, D., Zhang, M., & Johnston, M. (2010) "Investigation of Simplification Threshold and Noise Level of Input Data in Numerical Simplification of Genetic Programs". In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 3065-3072). Washington, DC: IEEE.
  • Kinzett, D., Zhang, M., & Johnston, M. (2010) "Analysis of Building Blocks with Numerical Simplification in Genetic Programming". In Lecture Notes in Computer Science (pp. 289-300). Springer Berlin Heidelberg. doi:10.1007/978-3-642-12148-7_25
  • Kinzett, D., Zhang, M., & Johnston, M. (2010) "Analysis of Building Blocks wih Numerical Simplification in Genetic Programming". In A. Esparcia-Alcazar, & E. al (Eds.), Proceedings of the 13th European Conference on Genetic Programming Vol. 6021 (pp. 289-300). Berlin: Springer-Verlag.
  • Khrypko, I., & Andreae, P. (2010) "Towards the Problem of Maintaining Suspense in Interative Narrative". In Proceedings of the 7th Australian Conference on Interactive Entertainment. Wellington: ACM.
  • Johnston, M., Liddle, T., & Zhang, M. (2010) "A Relaxed Approach to Simplification in Genetic Programming". In Lecture Notes in Computer Science (pp. 110-121). Springer Berlin Heidelberg. doi:10.1007/978-3-642-12148-7_10
  • Johnston, M., Liddle, T., & Zhang, M. (2010) "A Relaxed Approach to Simplification in Genetic Programming". In A. Esparcia-Alcazar, & E. al (Eds.), Proceedings of the 13th European Conference on Genetic Programming (pp. 110-121). Berlin: Springer-Verlag.
  • Hunt, R., Johnston, M., Browne, W., & Zhang, M. (2010) "Sampling Methods in Genetic Programming for Classification with Unbalanced Data". In J. Li (Ed.), Advances in Artificial Intelligence (Vol. 6464, pp. 273-282). Berlin: Springer-Verlag.
  • Gao, X., Andreae, P., & Man, S. (2010) "Clustering Log Files to Identify Common Malicious Networking Attacks". In The 2nd International Conference on Advanced Intelligence (pp. 145-152).
  • Fu, W., Johnston, M., & Zhang, M. (2010) "Hybrid Particle Swarm Optimisation Algorithms based on Differential Evolution and Local Search". In J. Li (Ed.), Advances in Artificial Intelligence (Vol. 6464, pp. 313-322). Berlin: Springer-Verlag.
  • Fu, H., Mei, Y., Tang, K., & Zhu, Y. (2010) "Memetic algorithm with heuristic candidate list strategy for capacitated arc routing problem". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. doi:10.1109/CEC.2010.5586042
  • Downey, C., Zhang, M., & Browne, W. (2010) "New Crossover Operators in Linear Genetic Programming for Multiclass Object Classification". In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 885-892). New York: ACM.
  • Creedy, J. (2010) "Preface". In Francis Ysidro Edgeworth: Portrait with Family and Friends. Cheltenham: Edward Elgar.
  • Chandra, R., Frean, M. R., & Zhang, M. (2010) "An Encoding Scheme for Cooperative Coevolutionary Feedforward Neural Networks". In Unknown Book (pp. 253-262).
  • Chandra, R., Frean, M., & Zhang, M. (2010) "A memetic framework for cooperative co-evolutionary feedforward neural networks". School of Engineering and Computer Science, Victoria University of Wellington.
  • Bhowan, U., Zhang, M., & Johnston, M. (2010) "Genetic Programming for Classification with Unbalanced Data". In A. Esparcia-Alcazar, & E. al (Eds.), Proceedings of the 13th European Conference on Genetic Programming (Vol. 6021, pp. 1-13). Berlin: Springer-Verlag.
  • Bhowan, U., Zhang, M., & Johnston, M. (2010) "AUC Analysis of the Pareto-Front using Multi-objective GP for Classification with Unbalanced Data". In M. Pelikan, & J. Branke (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 845-852). New York: ACM.
  • Bhowan, U., Zhang, M., & Johnston, M. (2010) "A Comparison of Classification Strategies in Genetic Programming with Unbalanced Data". In J. Li (Ed.), Advances in Artificial Intelligence (Vol. 6464, pp. 243-252). Berlin: Springer-Verlag.
  • Benson, E., & Andreae, P. (2010) "Sketch Interaction in Real Time Strategy Games". In Proceedings of the 7th Australian Conference on Interactive Entertainment. Wellington: ACM.
  • Bell, T., Andreae, P., & Lambert, L. (2010) "Computer Science in NZ High Schools". In T. Clear, & J. Hamer (Eds.), Proceedings of the Twelfth Australasian Computing Education Conference (ACE) (pp. 15-22). Brisbane: ACM.
  • Aziz, N. A. A., Mohemmed, A. W., & Zhang, M. (2010) "Particle Swarm Optimization for Coverage Maximization and Energy Conservation in Wireless Sensor Networks". In Lecture Notes in Computer Science (pp. 51-60). Springer Berlin Heidelberg. doi:10.1007/978-3-642-12242-2_6
  • Aziz, N., Mohemmed, A., & Zhang, M. (2010) "Particle Swarm Optimization for Coverage Maximization and Energy Conservation in Wireless Sensor Networks". In Proceedings of European Conference on the Applications of Evolutionary Computation 2010, The 7th European Event on the Application of Nature-inspired Techniques for Telcommunications Networks and other Parallel and Distributed Systems Vol. 6025 (pp. 51-60). Berlin: Springer.
  • Atkins, D., Browne, W., & Zhang, M. (2010) "Evolution of Aesthetically Pleasing Art Without Human-In-The-Loop". In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1-8). Washington, DC: IEEE.
  • Song, A., Chen, D., & Zhang, M. (2009) "Bloat control in genetic programming by evaluating contribution of nodes". In Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 (pp. 1893-1894). doi:10.1145/1569901.1570221
  • Ma, H., Schewe, K. -D., & Thalheim, B. (2009) "Web information systems design in the era of web 2". 0 and beyond. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (pp. 10). ACM. doi:10.1145/1806338.1806346
  • Wojnar, M., & Andreae, P. (2009) "HOPPER: A hierarchical planning agent for unpredictable domains". In Conferences in Research and Practice in Information Technology Series Vol. 91 (pp. 85-93).
  • Stanley, E., Mogin, P., & Andreae, P. (2009) "S". E.A.L. - A Query Language for Entity-Association Queries. In Conferences in Research and Practice in Information Technology Series Vol. 92 (pp. 69-78).
  • Li, L., Xue, B., Niu, B., Chai, Y., & Wu, J. (2009) "The novel non-linear strategy of inertia weight in particle swarm optimization". In BIC-TA 2009 - Proceedings, 2009 4th International Conference on Bio-Inspired Computing: Theories and Applications (pp. 183-187). doi:10.1109/BICTA.2009.5338130
  • Niu, B., Xue, B., Li, L., & Chai, Y. (2009) "Symbiotic multi-swarm PSO for portfolio optimization". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 5755 LNAI (pp. 776-784). doi:10.1007/978-3-642-04020-7_83
  • Li, L., Xue, B., Niu, B., Tan, L., & Wang, J. (2009) "A novel particle swarm optimization with non-linear inertia weight based on tangent function". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 5755 LNAI (pp. 785-793). doi:10.1007/978-3-642-04020-7_84
  • Kirley, M., Zhang, M., & Li, X. (2009) "Special issue on simulated evolution and learning". Evolutionary Intelligence, 2(4), 149-150. doi:10.1007/s12065-009-0033-0
  • Ma, H., & Wu, Y. (2009) "Grey Predictive on Natural Gas Consumption and Production in China". In 2009 Second Pacific-Asia Conference on Web Mining and Web-based Application (pp. 91-94). IEEE. doi:10.1109/wmwa.2009.26
  • Chen, G., Low, C. P., & Yang, Z. (2009) "Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms". IEEE Transactions on Evolutionary Computation, 13(3), 661-673. doi:10.1109/tevc.2008.2011742
  • Zhang, M., & Johnston, M. (2009) "A variant program structure in tree-based genetic programming for multiclass object classification". In S. Cagnoni (Ed.), Evolutionary Image Analysis and Signal Processing. Studies in Computational Intelligence (Vol. 213, pp. 55-72). Berlin: Springer. Retrieved from http://victoria.lconz.ac.
  • Zhang, M., Cagnoni, S., & Olague, G. (2009) "GECCO 2009 Tutorial: Evolutionary Computer Vision". In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation compainion (pp. 3355-3380). ACM Press.
  • Zhang, M., Cagnoni, S., & Olague, G. (2009) "Evolutionary computer vision". In Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 Vol. 2009-January (pp. 3355-3379). doi:10.1145/1570256.1570423
  • Zhang, M., Bhowan, U., & Ny, B. (2009) "Genetic programming for object detection: A two-phase approach with an improved fitness function". In Progress In Computer Vision And Image Analysis (pp. 447-470). doi:10.1142/9789812834461_0024
  • Xie, H., & Zhang, M. (2009) "Tuning Selection Pressure in Tournament Selection". Wellington: School of Engineering and Computer Science, Victoria University of Wellington.
  • Xie, H., & Zhang, M. (2009) "Sample Issues of Tournament Selection in Genetic Programming". Wellington: School of Engineering and Computer Science, Victoria University of Wellington.
  • Xie, H., & Zhang, M. (2009) "Balancing Parent and Offspring Selection in Genetic Programming". In A. Nicholson, & X. Li (Eds.), AI2009: Advances in Artificial Intelligence. Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09), Lecture Notes in Artificial Intelligence (Vol. 5866, pp. 454-464). Berlin: Springer.
  • Xie, H., & Zhang, M. (2009) "An Analysis and Evaluation of the Saving Capability and Feasibility of Backward-chaining Evolutionary Algorithms". In K. Korb, M. Randall, & T. Hendtlass (Eds.), Artificial Life: Borrowing from Biology. Proceedings of the 4th Australian Conference on Artificial Life (ACAL'09), Lecture Notes in Artificial Intelligence (Vol. 5865, pp. 63-72). Berlin: Springer.
  • Wojnar, M., & Andreae, P. (2009) "HOPPER: a hierarchical planning agent for unpredictable domains". In Proceedings of the Thirty-Second Australasian Computer Science Conference (ACSC 2009) Vol. 91 (pp. 73-81). Wellington, New Zealand.
  • Thalheim, B., Schewe, K. D., & Ma, H. (2009) "Conceptual application domain modelling". In Asia-Pacific Conference on Conceptual Modelllng (APCCM) Proceedings (pp. 49-57). Australian Computer Society.
  • Tang, K., Mei, Y., & Yao, X. (2009) "Memetic algorithm with extended neighborhood search for capacitated arc routing problems". IEEE Transactions on Evolutionary Computation, 13, 1151-1166. doi:10.1109/TEVC.2009.2023449
  • Stanley, E., Mogin, P., & Andreae, P. (2009) "S". E.A.L. - A Query Language for Entity-Association Queries. In A. Bouguettaya, & X. Lin (Eds.), Proceedings of the 20th Australasian Database Conference (ADC 2009) Vol. 92 (pp. 67-76). Wellington.
  • Song, A., Chen, D., & Zhang, M. (2009) "Bloat Control by Evaluating Contribution of Nodes". In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009) (pp. 1893-1894). ACM Press.
  • Setayesh, M., Zhang, M., & Johnston, M. (2009) "Feature Extraction and Detection of Simple Objects Using Particle Swarm Optimisation". Wellington: School of Engineering and Computer Science, Victoria University of Wellington.
  • Setayesh, M., Zhang, M., & Johnston, M. (2009) "A new homogeneity-based approach to edge detection using PSO". In Proceedings of the 24th International Conference on Image and Vision Computing New Zealand (pp. 231-236). Wellington: IEEE Press.
  • Nguyen, G., Gao, X., & Andreae, P. (2009) "Vietnamese Document Representation and Classification". In A. Nicholson, & X. Li (Eds.), AI 2009: Advances in Artificial Intelligence, 22nd Australasian Joint Conference (pp. 577-586). Berlin: Springer.
  • Nguyen, G., Gao, X., & Andreae, P. (2009) "Text Categorization for Vietnamese Documents". In Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology (pp. 466-469). Washington, DC: IEEE Computer Society.
  • Neshatian, K., & Zhang, M. (2009) "Unsupervised Elimination of Redundant Features Using Genetic Programming". In A. Nicholson, & X. Li (Eds.), AI2009: Advances in Artificial Intelligence. Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09), Lecture Notes in Artificial Intelligence 5866 (pp. 432-442). Berlin: Springer.
  • Neshatian, K., & Zhang, M. (2009) "Pareto Front Feature Selection: Using Genetic Programming to Explore the Feature Space". In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009) (pp. 1027-1034). Montreal: ACM Press.
  • Neshatian, K., & Zhang, M. (2009) "Input Space Transformation in Classification Problems Using Genetic Programming". In Proceedings of the 7th New Zealand Computer Science Research Student Conference.
  • Neshatian, K., & Zhang, M. (2009) "Genetic Programming for Feature Subset Ranking in Binary Classification Problems". In L. Vanneschi, S. Gustafson, A. Moraglio, I. De Falco, & M. Ebner (Eds.), Proceedings of the 12th European Conference on Genetic Programming (EuroGP 2009). Lecture Notes in Computer Science (Vol. 5481, pp. 121-132). Berlin: Springer.
  • Neshatian, K., & Zhang, M. (2009) "Dimensionality Reduction in Face Detection: A Genetic Programming Approach". In Proceedings of the 24th International Conference on Image and Vision Computing New Zealand (pp. 391-396). Wellington: IEEE Press.
  • Mohemmed, A., Zhang, M., & Johnston, M. (2009) "Particle Swarm Optimization Based Adaboost for Face Detection". In Proceedings of the 2009 IEEE Congress on Evolutionary Computation (pp. 2494-2501). Piscataway, NJ: IEEE Press.
  • Mohemmed, A., Johnston, M., & Zhang, M. (2009) "Particle Swarm Optimization Based Multi-prototype Ensembles". In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009) (pp. 57-64). ACM Press.
  • Mohemmed, A., Johnston, M., & Zhang, M. (2009) "Particle Swarm Optimisation Based AdaBoost for Object Detection". Wellington: School of Engineering and Computer Science, Victoria University of Wellington.
  • Miller, J., Chan, A., Giannakakou, P., Andreae, P., & Northcote, P. (2009) "Peloruside A inhibits microtubule dynamics in a breast cancer cell line". In ComBio 2009. Christchurch.
  • Mei, Y., Tang, K., & Yao, X. (2009) "Improved memetic algorithm for capacitated arc routing problem". In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 1699-1706). IEEE. doi:10.1109/CEC.2009.4983146
  • Mei, Y., Tang, K., & Yao, X. (2009) "A global repair operator for capacitated arc routing problem". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39, 723-734. doi:10.1109/TSMCB.2008.2008906
  • McGaughran, D., & Zhang, M. (2009) "Evolving More Representative Programs with Genetic Programming". International journal of Software Engineering and Knowledge Engineering, 19(1), 1-22.
  • Ma, H., Schewe, K. D., & Wang, Q. (2009) "An Abstract Model for Service Provision, Search and Composition". In IEEE Asia-Pacific Services Computing Conference Proceedings (pp. 95-102). New York: IEEE Computet Society.
  • Ma, H., Schewe, K. D., Thalheim, B., & Wang, Q. (2009) "A Service-Oriented Approach to Web Warehousing". In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services (pp. 96-103). ACM and Austrian Computer Society.
  • Ma, H., Schewe, K. D., Thalheim, B., & Wang, Q. (2009) "A Service -Oriented Approach to Web Warehousing". In 11th International Conference on information Integration and Web-based Applications & Services ii WAS 2009, Eds.: G. Kotsis, D Taniar, E. Pardede, I. Khalil), ACM and Austrian Computer Society (pp. 94-101).
  • Ma, H., Schewe, K. -D., & Thalheim, B. (2009) "Storyboarding – High-Level Engineering of Web Information Systems". In Lecture Notes in Computer Science (pp. 7-8). Springer Berlin Heidelberg. doi:10.1007/978-3-642-04409-0_4
  • Ma, H., Schewe, K. D., & Thalheim, B. (2009) "Modelling and Maintenance of Very Large Database Schemata Using Meta-Structures". In Proceedings of the 8th International Conference on Information Systems Technology and its Applications (ISTA) (pp. 17-28).
  • Ma, H., Schewe, K. D., & Thalheim, B. (2009) "Geometrically Enhanced Conceptual Modelling". In A. Laender, & E. al (Eds.), Proceedings of the 28th International Conference on Conceptual Modeling (ER 2009) Vol. 5829 (pp. 219-233). Springer.
  • Ma, H., Noack, R., & Schewe, K. D. (2009) "Algebriac meta-structure handling of huge database schemata". In Conceptual Modelling in the Large Conference Proceedings (pp. 12-21). Berlin: Springer.
  • Ma, H., Noack, R., & Schewe, K. D. (2009) "Algebraic Meta-Structure Handling of Huge Database Schemata". In Conceptual Modelling in the Large Vol. 5833 (pp. 12-21). Springer.
  • Ma, H. (2009) "A theory of Data-Intensive Software Services". Service-Oriented Computing and Applications, 3(4), 263-283.
  • Le, V., Komisarczuk, P., & Gao, X. (2009) "Applying AI to improve the performance of Client Honeypots". In PAM 2009: Proceedings of the 10th International Conference on Passive and Active Measurement. Seoul, Republic of Korea.
  • Kirley, M., Zhang, M., & Li, X. (2009) "Special Issue on Simulated Evolution and Learning". Evolutionary Intelligence, 2(4), 149-150. doi:10.1007/s12065-009-0033-0
  • Kinzett, D., Johnston, M., & Zhang, M. (2009) "Numerical Simplification for Bloat Control and Analysis of Building Blocks in Genetic Programming". Wellington: School of Engineering and Computer Science, Victoria University of Wellington.
  • Kinzett, D., Johnston, M., & Zhang, M. (2009) "Numerical simplification for bloat control and analysis of building blocks in genetic programming". Evolutionary Intelligence, 2(4), 151-168. doi:10.1007/s12065-009-0029-9
  • Kinzett, D., Johnston, M., & Zhang, M. (2009) "How Online Simplification Affects Building Blocks in Genetic Programming". In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009) (pp. 979-986). ACM Press.
  • Downey, C., & Zhang, M. (2009) "Multiclass Object Classification for Computer Vision using Linear Genetic Programming". In D. Bailey (Ed.), Proceedings of the 24th International Conference on Image and Vision Computing New Zealand (pp. 73-78).
  • Chandra, R., Zhang, M., & Rolland, L. (2009) "A Hybrid Meta-Heuristic Approach to the Forward Kinematics of 3RPR Planar Parallel Manipulator". In Proceedings of the 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA2009) (pp. 177-182). Daejeon, Korea. doi:10.1109/CIRA.2009.5423213
  • Chandra, R., Frean, M., Zhang, M., & Omlin, C. (2009) "Building Subcomponents in the Cooperative Coevolution Framework for Training Recurrent Neural Networks: Technical Report". __.
  • Cassell, K., Andreae, P., Groves, L., & Noble, J. (2009) "Towards Automating Class-Splitting Using Betweenness Clustering". In ASE 2009 (pp. 595-599). Washington, DC: IEEE Computer Society.
  • Bhowan, U., Zhang, M., & Johnston, M. (2009) "Multi-Objective Genetic Programming for Classification with Unbalanced Data". In A. Nicholson, & X. Li (Eds.), AI 2009: Advances in Artificial Intelligence. Proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence (AI'09), Lecture Notes in Artificial Intelligence (Vol. 5866, pp. 370-380). Berlin: Springer.
  • Bhowan, U., Zhang, M., & Johnston, M. (2009) "Genetic Programming for Image Classification with Unbalanced Data". In Proceedings of the 24th International Conference on Image and Vision Computing New Zealand (pp. 316-321). Wellington: IEEE Press.
  • Bhowan, U., Johnston, M., & Zhang, M. (2009) "Differentiating Between Individual Class Performance in Genetic Programming Fitness for Classification with Unbalanced Data". In Proceedings of the 2009 IEEE Congress on Evolutionary Computation (pp. 2802-2809). IEEE Press.
  • Zhou, R., Wei, R., Chen, G., Yang, Z., Shen, H., Zhang, J., & Luo, M. (2008) "Ant Colony Inspired Self-Healing for Resource Allocation in Service-Oriented Environment Considering Resource Breakdown". In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 66-69). IEEE. doi:10.1109/wiiat.2008.105
  • Zhou, R., Chen, G., Yang, Z. H., Luo, M., Zhang, J. B., & Tan, C. H. (2008) "Self-organized manufacturing resource management: An ant-colony inspired approach". In 2008 10th International Conference on Control, Automation, Robotics and Vision (pp. 904-909). IEEE. doi:10.1109/icarcv.2008.4795638
  • Zhang, M. (2008) "Genetic programming techniques for multiclass object recognition". Eurasip Book Series on Signal Processing and Communications, 8, 349-370.
  • Ren, W., Chen, G., Zhonghua Yang., Junhong Zhou., Jing Bing Zhang., Chor Ping Low., . . . Chengzheng Sun. (2008) "Semantic enhanced rule driven workflow execution in Collaborative Virtual Enterprise". In 2008 10th International Conference on Control, Automation, Robotics and Vision (pp. 910-915). IEEE. doi:10.1109/icarcv.2008.4795639
  • Ren, W., Chen, G., Shen, H., Yang, Z., Zhang, J. B., Low, C. P., . . . Sun, C. (2008) "Dynamic Self-Healing for Service Flows with Semantic Web Services". In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 598-604). IEEE. doi:10.1109/wiiat.2008.111
  • Niu, B., Li, L., Ma, L., & Xue, B. (2008) "Design of Logistic Monitoring Management System Based on RFID Technology". In 2008 International Symposium on Intelligent Information Technology Application Workshops (pp. 669-672). IEEE. doi:10.1109/iita.workshops.2008.183
  • Xie, H., Zhang, M., Andreae, P., & Johnston, M. (2008) "Is the not-sampled issue in tournament selection critical?". In 2008 IEEE Congress on Evolutionary Computation, CEC 2008 (pp. 3710-3717). doi:10.1109/CEC.2008.4631300
  • Xie, H., Zhang, M., & Andreae, P. (2008) "An analysis of the distribution of swapped subtree sizes in tree-based genetic programming". In 2008 IEEE Congress on Evolutionary Computation, CEC 2008 (pp. 2859-2866). doi:10.1109/CEC.2008.4631181
  • Wone, P., & Zhang, M. (2008) "SCHEME: Caching subtrees in genetic programming". In 2008 IEEE Congress on Evolutionary Computation, CEC 2008 (pp. 2678-2685). doi:10.1109/CEC.2008.4631158
  • Smart, W., & Zhang, M. (2008) "Empirical analysis of schemata in Genetic Programming using maximal schemata and MSG". In 2008 IEEE Congress on Evolutionary Computation, CEC 2008 (pp. 2983-2990). doi:10.1109/CEC.2008.4631200
  • Mohemmed, A. W., & Zhang, M. (2008) "Evaluation of particle swarm optimization based centroid classifier with different distance metrics". In 2008 IEEE Congress on Evolutionary Computation, CEC 2008 (pp. 2929-2932). doi:10.1109/CEC.2008.4631192
  • Wei Ren., Gang Chen., Zhonghua Yang., Jing Bing Zhang., Chor Ping Low., Chen, D., & Chengzheng Sun. (2008) "Self-healing capable workflow execution with Semantic Web service". In 2008 IEEE International Conference on Service Operations and Logistics, and Informatics (pp. 508-513). IEEE. doi:10.1109/soli.2008.4686448
  • Link, S., Ma, H., & Yang, J. (2008) "E-BAG 2008 workshop PC chairs' message (Vol". 5176 LNCS). doi:10.1007/978-3-540-85200-1_1
  • Chen, G., Ren, W., Zhang, J. B., Yang, Z., Low, C. P., Sun, C., & Chen, D. (2008) "Developing Rule-Enhanced Dynamic Virtual Enterprise Integration Frameworks". In 2008 IEEE 8th International Conference on Computer and Information Technology Workshops (pp. 694-699). IEEE. doi:10.1109/cit.2008.workshops.57
  • Gang Chen., Chor Ping Low., & Zhonghua Yang. (2008) "Enhancing Search Performance in Unstructured P2P Networks Based on Users' Common Interest". IEEE Transactions on Parallel and Distributed Systems, 19(6), 821-836. doi:10.1109/tpds.2008.42
  • Chen, G., Ren, W., Zhang, J. B., Yang, Z., Low, C. P., Sun, C., & Chen, D. (2008) "Dynamic virtual enterprise integration via business rule enhanced semantic service composition framework". In 2008 3rd IEEE Conference on Industrial Electronics and Applications (pp. 1174-1179). IEEE. doi:10.1109/iciea.2008.4582703
  • Gang Chen., Chor Ping Low., & Zhonghua Yang. (2008) "Coordinated Services Provision in Peer-to-Peer Environments". IEEE Transactions on Parallel and Distributed Systems, 19(4), 433-446. doi:10.1109/tpds.2007.70745
  • Zhang, Y., & Andreae, P. (2008) "Iterative Neighbourhood Similarity Computation for Collaborative Filtering". In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 806-809). Sydney: IEEE Computer Society.
  • Zhang, M., & Wong, P. (2008) "Genetic Programming for Medical Classification: A Program Simplification Approach". Genetic Programming and Evolvable Machines, 9(3), 229-255.
  • Zhang, M., & Wong, P. (2008) "Explicitly Simplifying Evolved Genetic Programs During Evolution". International Journal of Computational Intelligence and Applications, 7(2), 201-232.
  • Xie, H., Zhang, M., Andreae, P., & Johnston, M. (2008) "Is the not-sampled issue in tournament selection critical?". In Proceedings of the 2008 IEEE Congress on Evolutionary Computation (pp. 3711-3718). Hong Kong: IEEE Press.
  • Xie, H., Zhang, M., Andreae, P., & Johnston, M. (2008) "FAn Analysis of Multi-Sampled Issue and No-Replacement Tournament Selection". In Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2008) (pp. 1323-1330). Atlanta, USA: ACM Press.
  • Xie, H., Zhang, M., Andreae, P., & Johnston, M. (2008) "An Analysis of Multi-Sampled Issue and No-Replacement Tournament Selection". In Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2008) (pp. 1323-1330). Atlanta, USA: ACM Press.
  • Xie, H., Zhang, M., & Andreae, P. (2008) "An Analysis of the Distribution of Swapped Subtree Sizes in Tree-based Genetic Programming". In Proceedings of the 2008 IEEE Congress on Evolutionary Computation (pp. 2864-2971). Hong Kong: IEEE Press.
  • Wong, P., & Zhang, M. (2008) "SCHEME: Caching Subtrees in Genetric Programming". In Proceedings of the 2008 IEEE Congress on Evolutionary Computation (pp. 2683-2690). Hong Kong: IEEE Press.
  • Wobcke, W., & Zhang, M. (2008) "AI 2008: Advances in Artificial Intelligence: 21st Australasian Joint Conference on Artificial Intelligence". Auckland: Springer.
  • Smart, W., & Zhang, M. (2008) "Emperical Analysis of Schemata in Genetic Programming using Maximal Schemata and MSG". In Proceedings of the 2008 IEEE Congress on Evolutionary Computation (pp. 2988-2995). Hong Kong: IEEE Press.
  • Neshatian, K., Zhang, M., & Andreae, P. (2008) "Genetic Programming for Feature Ranking in Classification Problems". In X. Li, & E. al (Eds.), Proceedings of the seventh International Conference on Simulated Evolution and Learning (SEAL'08). Vol. 5361 (pp. 544-554). Berlin: Springer.
  • Neshatian, K., & Zhang, M. (2008) "Genetic Programming for Performance Improvement and Dimensionality Reduction of Classification problems". In 2008 IEEE Congress on Evolutionary Computation (pp. 2811-2818). Hong Kong: IEEE Press. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.
  • Neshatian, K., & Zhang, M. (2008) "Genetic Programming and Class-wise Orthogonal Transformation for Dimension Reduction in Classification Problems". In Proceedings of the 11th European Conference on Genetic Programming Vol. 4971 (pp. 242-253). Naples, Italy: Springer.
  • Mohemmed, A., Zhang, M., & Sahoo, N. (2008) "Cooperative Particle Swarm Optimization for the Delay Constrained Least Cost Path Problem". In J. van Hemert, & C. Cotta (Eds.), Evolutionary Computation in Combinatorial Optimization. lecture notes in Computer Science (Vol. 4972, pp. 25-35). Naple, Italy: Springer.
  • Mohemmed, A., Zhang, M., & Johnston, M. (2008) "A PSO based AdaBoost approach to object detection". In X. Li (Ed.), Proceedings of the seventh International Conference on Simulated Evolution and Learning Vol. 5361 (pp. 81-90). Berlin: Springer.
  • Mohemmed, A., & Zhang, M. (2008) "Evaluation of Particle Swarm Optimization Based Centroid Classifier with Different Distance Metrics". In Proceedings of the 2008 IEEE Congress on Evolutionary Computation (pp. 2934-2937). Hong Kong: IEEE Press.
  • Ma, H., Schewe, K. -D., Thalheim, B., & Wang, Q. (2008) "Abstract State Services". In Lecture Notes in Computer Science (pp. 406-415). Springer Berlin Heidelberg. doi:10.1007/978-3-540-87991-6_48
  • Ma, H., Schewe, K. D., Thalheim, B., & Wang, Q. (2008) "Abstract State Services, A Theory of Web Services". In I. Y. Song, & E. al (Eds.), Advances in Conceptual Modelling - Challenges and Opportunities, Lecturer Notes in Computer Science (Vol. 5232, pp. 406-415). Springer.
  • Ma, H., Schewe, K. D., & Thalheim, B. (2008) "Context Analysis: Towards Pragmatics of Web Information Systems Design". In A. Hinze, & M. Kirchberg (Eds.), Proceedings of the Asia-Pacific Conference on Conceptual Modelling, CRPIT Vol. 79 (pp. 1-10). Sydney: Australian Computer Society.
  • Li, X., Kirley, M., Zhang, M., Green, D., Ciesielski, V., Abbass, H., . . . Shi, Y. (2008) "The Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL 2008)". Melbourne: Springer.
  • Kinzett, A., Zhang, M., & Johnston, M. (2008) "Using numerical simplification to control bloat in genetic programming". In Proceedings of the seventh International Conference on Simulated Evolution and Learning Vol. 5361 (pp. 493-502).
  • Gao, X., Le, P., & Zhang, M. (2008) "Detecting Data Records in Semi-structured Web Sites based on Text Token Clustering". Integrated Computer-Aided Engineering, 15(4), 297-311.
  • Evans, H., & Zhang, M. (2008) "Particle Swarm Optimisation for Object Classification". In 23rd International Conference Image and Vision Computing New Zealand (pp. 1-6). Washington, DC: IEEE Press.
  • Chin, B., & Zhang, M. (2008) "Object Detection using Neural Networks and Genetic Programming". In Applications of Evolutionary Computing, Lecture Notes in Computer Science (Vol. 4974, pp. 335-340). Naple, Italy: Springer.
  • Andreae, P., Xie, H., & Zhang, M. (2008) "Genetic Programming for Detecting Rhythmic Stress in Spoken English". International Journal of Knowledge-Based and Intelligent Engineering Systems. Special Issue on Genertic Programming, 12(1), 15-28.
  • Crabtree, D., Andreae, P., & Gao, X. (2007) "Understanding query aspects with applications to interactive query expansion". In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007 (pp. 691-695). doi:10.1109/WI.2007.4427175
  • Zhang, M. (2007) "Improving object detection performance with genetic programming". International Journal on Artificial Intelligence Tools, 16(5), 849-873. doi:10.1142/S0218213007003576
  • Zhou, J., Cooper, K., Ma, H., & Yen, I. L. (2007) "On the customization of components: A rule-based approach". IEEE Transactions on Knowledge and Data Engineering, 19(9), 1262-1274. doi:10.1109/TKDE.2007.1059
  • Xie, H., Zhang, M., & Andreae, P. (2007) "Another investigation on tournament selection: Modelling and visualisation". In Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference (pp. 1468-1475). doi:10.1145/1276958.1277226
  • Chen, G., Zhang, J. B., Low, C. P., Yang, Z., Ren, W., & Zhuang, L. (2007) "Collaborative Virtual Enterprise Integration via Semantic Web Service Composition". In 2007 2nd IEEE Conference on Industrial Electronics and Applications (pp. 1409-1414). IEEE. doi:10.1109/iciea.2007.4318638
  • Zhang, M., Gao, X., & Lou, W. (2007) "A new crossover operator in genetic programming for object classification". IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 37(5), 1332-1343. doi:10.1109/TSMCB.2007.902043
  • Zhang, M., Fogelberg, C., & Ma, Y. (2007) "A linear structured approach and a refined fitness function in genetic programming for multi-class object classification". Connection Science, 19(4), 339-359. doi:10.1080/09540090701725557
  • Zhang, M., & Fogelberg, C. (2007) "Genetic Programming for image recognition: an LGP approach". In M. Giacobini, & E. al (Eds.), Applications of Evolutionary Computing: EvoWorkshops 2007: EvoCOMNET, EVoFIN, EvoISAP, EvolINTERACTION, EvoMUSART, EvoSTOC and EvoTRANSLOG, Valencia, Spain, April 11-13, 2007 Proceedings, Lecture Notes in Computer Science Vol. 4448 (pp. 340-350). Berlin: Springer. doi:10.1007/978-3-540-71805-5
  • Zhang, M., Bhowan, U., & Ny, B. (2007) "Genetic Programming for Object Detection: A Two-Phase Approach with an Improved Fitness Function". Electronic Letters on Computer Vision and Image Analysis, 6(1), 27-43.
  • Zhang, M. (2007) "Improving object detection performance with Genetic Programming". International Journal on Artificial Intelligence Tools, 16(5), 849-873. doi:10.1142/S0218213007003576
  • Zhang, M. (2007) "Genetic Programming Techniques for Multi-class Object Recognition". In S. Cagnoni, E. Lutton, & G. Olague (Eds.), Genetic and Evolutionary Computation for Image Processing and Analysis (Vol. 8, pp. 415-440). New York: Hindawi Publishing Corporation.
  • Zhang, M. (2007) "Genetic Programming for Object Detection". In Proceedings of the 21st International Conference on Image and Vision Computing New Zealand (pp. 435-440).
  • Zhang, M. (2007) "Genetic algorithms and neural networks for object detection". In S. Cagnoni, E. Lutton, & G. Olague (Eds.), Eurasip Book Series vol. 8 : Genetic and Evolutionary Computation for Image Processing and Analysis (Vol. 8, pp. 415-440). New York: Hindawi Publishing Corporation. Retrieved from http://www.hindawi.com/spc.8.
  • Xie, H., Zhang, M., & Andreae, P. (2007) "Not-sampled issue and round-replacement tournament selection". Wellington: University of Wellington, SMSCS.
  • Xie, H., Zhang, M., & Andreae, P. (2007) "Genetic Programming for New Zealand {CPI} inflation prediction". In D. Srinivasan, & L. Wang (Eds.), 2007 IEEE Congress on Evolutionary Computation (CEC 2007) (pp. 2538-2545). Singapore: IEEE Press. doi:10.1109/cec.2007.4424790
  • Xie, H., Zhang, M., & Andreae, P. (2007) "An analysis of depth of crossover points in tree-based Genetic Programming". In Proceedings of the 2007 IEEE Congress on Evolutionary Computation (pp. 4561-4568). Singapore: IEEE Press. doi:10.1109/CEC.2007.4425069
  • Xie, H., Zhang, M., & Andreae, P. (2007) "An analysis of constructive crossover and selection pressure in Genetic Programming". In D. Thierens (Ed.), Genetic and Evolutionary Computation Conference (GECCO'07) Vol. 2 (pp. 1739-1746). New York: ACM (Assoc. for Computing Machinery).
  • Wong, P., & Zhang, M. (2007) "Numerical-node building block analysis of genetic programming with simplification". In D. Theirens (Ed.), GECCO Genetic And Evolutionary Computation Conference 2007 Vol. 2 (pp. 1761). New York: ACM.
  • Wong, P., & Zhang, M. (2007) "Effects of program simplification on simple building blocks in Genetic Programming". In Proceedings of the 2007 IEEE Congress on Evolutionary Computation (pp. 1570-1577). Singapore: IEEE Press. doi:10.1109/CEC.2007.4424660
  • Smart, W., Andreae, P., & Zhang, M. (2007) "Empirical analysis of GP tree-fragments". In M. Ebner, & E. al (Eds.), Genetic Programming: 10th European Conference, EuroGP 2007, Valencia, Spain, April 11-13, 2007 Proceedings, Lecture Notes in Computer Science Vol. 4445 (pp. 55-67). Berlin: Springer. doi:10.1007/978-3-540-71605-1
  • Roehr, T., Carnegie, D., & Andreae, P. (2007) "Developing a Robust Control System for a Team of Autonomous Mobile Robots". In D. Carnegie, & P. Teal (Eds.), Proceedings of the Fourteenth Electronics New Zealand Conference: ENZCom 2007 (pp. 273-278). Wellington: Electronics New Zealand.
  • Ren, W., Chen, G., Chen, D., Low, C. P., Sun, C., Zhang, J. B., & Yang, Z. (2007) "Searching for Service-Oriented Strategies of Dynamic Composition of Web Services: A Comparative Perspective". In IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society (pp. 2615-2620). IEEE. doi:10.1109/iecon.2007.4460369
  • Patterson, G., & Zhang, M. (2007) "Fitness Functions in Genetic Programming for Classification with Unbalanced Data". In M. Orgun, & J. Thornton (Eds.), Proceedings of the 20th Australian Joint Conference on Artificial Intelligence, Lecture Notes in Artificial Intelligence Vol. 4830 (pp. 769-775). Berlin: Springer. doi:10.1007/978-3-540-76928-6_90
  • Neshatian, K., Zhang, M., & Johnston, M. (2007) "Feature construction and dimension reduction using Genetic Programming". In M. Orgun, & J. Thornton (Eds.), AI 2007: Advances in Artificial Intelligence 20th Australian Joint Conference on Artificial Intelligence Gold Coast, Australia, December 2-6, 2007 Proceedings Vol. 4830 (pp. 160-170). Berlin: Springer.
  • Neshatian, K., Zhang, M., & Johnston, M. (2007) "Feature Construction and Dimension Reduction Using Genetic Programming". In M. Orgun, & J. Thornton (Eds.), AI 2007: Advances in Artificial Intelligence, 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007, Proceedings (Vol. 4830, pp. 160-170). Berlin: Springer. doi:10.1007/978-3-540-76928-6_18
  • McIntosh, J. (2007) "Foreword". In Architectural Practice: notes on current issues 2007 (pp. 1-2). Wellington: School of Architecture, Victoria University of Wellington.
  • Ma, X., Teng, G., & Zhang, M. (2007) "Carbon Potential Prediction Using Genetic Programming". Control and Automation, 23(9), 239-241.
  • Ma, H., Schewe, K. D., & Wang, Q. (2007) "A Heuristic Approach to Cost-Efficient Derived Horizontal Fragmentation of Complex Value Databases". In Proceedings of the Australian Database Conference, CRPIT Vol. 63 (pp. 103-112). Australian Computer Society.
  • Ma, H., Schewe, K. D., & Kirchberg, M. (2007) "A Heuristic Approach to Fragmentation Incorporating Query Information". In Databases and Information Systems - Frontiers in Artificial Intelligence and Applications (Vol. 155, pp. 103-116). Amsterdam: IOS Press.
  • Ma, H., Schewe, K., & Wang, Q. (2007) "Distribution design for higher-order data models". Data and Knowledge Engineering, 60(2), 400-434.
  • Ma, H., Noack, R., Riaz-ud-Din, F., Schewe, K. D., & Thalheim, B. (2007) "Capturing forms in web information systems". In Innovations'07: 4th International Conference on Innovations in Information Technology, IIT (pp. 198-202). doi:10.1109/IIT.2007.4430382
  • Ma, H., Noack, R., Riaz-ud-Din, F., Schewe, K. D., & Thalheim, B. (2007) "Capturing Forms in Web Information Systems". In Proceedings of the 4th International Conference on Innovations in Information Technology (pp. 1-5). New York: IEEE Computer Society.
  • Ma, H., & Kirchberg, M. (2007) "Cost-based fragmentation for distributed complex value databases". In C. Parent, K. D. Schewe, V. Storey, & B. Thalheim (Eds.), Lecture Notes in Computer Science: Proceedings of the 26th International Conference on Conceptual Modelling Vol. 4801 (pp. 72-86). Berlin: Springer-Verlag.
  • Ma, H. (2007) "Distribution Design for Complex-value Databases". (PhD Thesis).
  • Li, X., Zhang, J., Zhang, M., & Zhao, L. (2007) "An approach to identification of weed in wheat fields". Journal of Agricultural Mechanization Research, 2007(5), 64-68.
  • Le, P., & Gao, X. (2007) "Using clustering for web information extraction". In M. Orgun, & J. Thornton (Eds.), AI 2007: Advances in Artificial Intelligence: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007, Proceedings (pp. 415-424). Berlin: Springer.
  • Krawiec, K., Howard, D., & Zhang, M. (2007) "Overview of Object Detection and Image Analysis by Means of Genetic Programming Techniques". In D. Howard, P. Kyu Rhee, S. Halgamuge, & S. J. Yoo (Eds.), Proceedings of the 2007 International Conference Frontiers in the Convergence of Bioscience and Information Technologies (pp. 779-784). Los Almitos: IEEE CS Press.
  • Huayang, X., Zhang, M., & Andreae, P. (2007) "Another investigation on tournament selection: modelling and visulisation". In Genetic and evolutionary Computation Conference (pp. 1468-1475). ACM Press. doi:10.1145/276958.1277226
  • Hartmann, S., Ma, H., & Schewe, K. D. (2007) "Cost-Based Vertical Fragmentation for XML". In Proceedings of the International Workshop on Database Management and Application over Networks, Lecture Notes in Computer Science Vol. 4537 (pp. 12-14). Springer.
  • Gao, X., Le, P., & Zhang, M. (2007) "Automatic data record detection in web pages". In Z. Zhang, & J. Siekmann (Eds.), Knowledge Science, Engineering and Management: Second International Conference, KSEM 2007, Melbourne Australia, November 28-30 2007 Proceedings Vol. 4798 (pp. 349-361). Berlin: Springer.
  • Crabtree, D., Andreae, P., & Gao, X. (2007) "Understanding Query Aspects with applications to Interactive Query Expansion". In T. Lin, & E. al (Eds.), Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI 2007) (pp. 691-695). Los Alamitos: IEEE Computer Society. doi:10.1109/WI.2007.30
  • Crabtree, D., Andreae, P., & Gao, X. (2007) "QC4 - A Clustering Evaluation Method". In Z. H. Zhou, H. Li, & Q. Yang (Eds.), Advances in Knowledge Discovery and Data Mining, 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007, Proceedings (pp. 59-70). Berlin: Springer.
  • Crabtree, D., Andreae, P., & Gao, X. (2007) "Exploiting Underrepresented Query Aspects for Automatic Query Expansion". In P. Berkhin, R. Caruana, X. Wu, & S. Gaffney (Eds.), Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 191-200). New York: ACM.
  • Chin, B., & Zhang, M. (2007) "Object detection using neural networks and genetic programming". Wellington: Victoria University of Wellington, SMSCS.
  • Chen, G., Ren, W., Chen, D., Zhang, J. B., Sun, C., Yang, Z., . . . Zhuang, L. (2007) "A Top-Down Methodology for Building Semantic-Rich Service-Oriented Collaborative Virtual Enterprise (CVE)". In IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society (pp. 2621-2626). IEEE. doi:10.1109/iecon.2007.4460187
  • Zhang, M., Gao, X., & Lou, W. (2006) "Looseness controlled crossover in GP for object recognition". In 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 1285-1292).
  • Chen, G., Yang, Z., & Low, C. P. (2006) "Coordinating Agents in Shop Floor Environments From a Dynamic Systems Perspective". IEEE Transactions on Industrial Informatics, 2(4), 269-280. doi:10.1109/tii.2006.885190
  • Gao, T., Ma, H., Yen, I. L., Khan, L., & Bastani, F. (2006) "A repository for component-based embedded software development". International Journal of Software Engineering and Knowledge Engineering, 16(4), 523-552. doi:10.1142/S0218194006002872
  • Ma, H., Yen, I. L., Zhou, J., & Cooper, K. (2006) "QoS analysis for component-based embedded software: Model and methodology". Journal of Systems and Software, 79(6), 859-870. doi:10.1016/j.jss.2005.10.001
  • Stojmirović, A., Andreae, P., Boland, M., Jordan, T. W., & Pestov, V. G. (2006) "PFMFind: a system for discovery of peptide homology and function". __. doi:10.48550/arxiv.q-bio/0603011
  • Zhao, J., & Ma, H. (2006) "ASM-Based Design of Data Warehouses and On-Line Analytical Processing Systems". Journal of Software and Systems, 79(5), 613-629.
  • Zhang, M., Wong, P., & Qian, D. (2006) "Online Program Simplification in Genetic Programming". In T. D. Wang, X. Li, S. H. Chen, X. Wang, H. Abbass, H. Iba, . . . X. Yao (Eds.), Simulated Evolution and Learning, Lecture Notes in Computer Science (LCNS) (Vol. 4247, pp. 592-600). Berling: Springer.
  • Zhang, M., & Smart, W. (2006) "Using Gaussian Distribution to Construct Fitness Functions in Genetic Programming for Multiclass Object Classification". Pattern Recognition Letters: Special issue: Evolutionary computer vision and image understanding, 27(11), 1266-1274. doi:10.1016/j.patrec.2005.07.024
  • Zhang, M., Lett, M., & Ma, Y. (2006) "Refining Fitness Functions and Optimising Training Data in GP for Object Detection". In T. D. Wang, X. Li, S. H. Chen, X. Wang, H. Abbass, H. Iba, . . . X. Yao (Eds.), Simulated Evolution and Learning, Lecture Notes in Computer Science (Vol. 4247, pp. 601-608). Berlin: Springer.
  • Zhang, M., & Lett, M. (2006) "Localisation fitness in GP for object detection". In Applications of Evolutionary Computing, Lecture Notes in Computer Science Vol. 3907 (pp. 472-483). Berlin: Springer. doi:10.1007/11732242_42
  • Zhang, M., & Lett, M. (2006) "Genetic Programming for Object Detection: Improving Fitness Functions and Optimising Training Data". IEEE Intelligent Informatics Bulletin, 7(1), 12-21.
  • Zhang, M., Gao, X., Lou, W., & Qian, D. (2006) "Investigation of Brood Size in GP with Brood Recombination Crossover for Object Recognition". In Q. Yang, & G. Webb (Eds.), Lecture Notes in Artificial Intelligence Vol. 4099 (pp. 923-928). Berlin: Springer.
  • Zhang, M., Gao, X., & Lou, W. (2006) "Looseness Controlled Crossover in GP for Object Classification". In Proceedings of IEEE Congress on Evolutionary Computation, a part of IEEE Congress on Computational Intelligence (pp. 4428-4435). Vancouver BC, Canada: IEEE Press.
  • Zhang, M., Gao, X., & Lou, W. (2006) "GP for Object Classification: Brood Size in Brood Recombination Crossover". In Lecture Notes in Computer Science (pp. 274-284). Springer Berlin Heidelberg. doi:10.1007/11941439_31
  • Zhang, M., Gao, X., & Lou, W. (2006) "GP for Object Classification: Brood Size in Brood Recombination Crossover". In A. Sattar, & B. Kong (Eds.), Proceedings of the 19th Australian Joint Conference on Artificial Intelligence, Lecture Notes in Computer Science (pp. 274-284). Berlin: Springer.
  • Zhang, M., Gao, X., Cao, M., & Ma, Y. (2006) "Neural Networks for Scientific Paper Classification". In Proceedings of the 2006 IEEE International Conference on Innovation Computing, Information and Control Vol. 2 (pp. 51-54). IEEE Press.
  • Zhang, M., Gao, X., Cao, M., & Ma, Y. (2006) "Modeling Citation Networks for Improved Scientific Paper Classification Performance". In Q. Yang, & G. Webb (Eds.), Proceedings of the 9th Paciific Rim International Conference on Artificial Intelligence Vol. 4099 (pp. 413-422). Berling: Springer.
  • Xie, H., Zhang, M., & Andreae, P. (2006) "Population Clustering in Genetic Programming". In P. Collet, M. Tomassini, M. Ebner, S. Gustafson, & A. Ekar (Eds.), Lecture Notes in Computer Science (Vol. 3905, pp. 190-201). Berlin: Springer. doi:10.1007/11729976_17
  • Xie, H., Zhang, M., & Andreae, P. (2006) "Genetic Programming for Automatic Stress Detection in Spoken English". In Applications of Evolutionary Applications, Lecture Notes in Computer Science (Vol. 3907, pp. 460-471). Berlin: Springer. doi:10.1007/11732242_41
  • Xie, H., Zhang, M., & Andreae, P. (2006) "Automatic Selection Pressure Control in Genetic Programming". In B. Yang, & Y. Chen (Eds.), Proceedings of the Sixth International Conference on Intelligent System Design and Applications (ISDA'06) (pp. 435-440). Washington, DC: IEEE Press. doi:10.1109/ISDA.2006.116
  • Xie, H., Zhang, M., & Andreae, P. (2006) "A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming". In Proceedings of IEEE Congress on Evolutionary Computation. Vancouver BC, Canada (pp. 2661-2668). Piscataway, NJ: IEEE Computer Soc. Press.
  • Wong, P., & Zhang, M. (2006) "Algebraic Simplification of GP Programs during Evolution". In M. Keijer, & E. al (Eds.), GECCO 06 : Genetic and Evolutionary Computation Conference Vol. 1 (pp. 927-934). New York: ACM.
  • Stojmirovic, A., Andreae, P., Boland, M., Jordan, T., & Pestov, V. (2006) "PFMFind: a system for discovery of peptide homology and function [Computer Software]". arXiv (Quantitative Biology).
  • Qian, J., Qian, D., & Zhang, M. (2006) "A Digit Recognition System for Paper Currency Identification Based on Virtual Instruments". In Proceedings of the 2nd International Conference on Information Automation (ICIA'06) (pp. 228-233). IEEE Press.
  • Ma, H., Schewe, K. D., & Zhao, J. (2006) "View Integration in Data Warehouse Design Using Typed Abstract State Machines and Strong Data Refinement". In Proceedings of the International Conference on Quality Software (pp. 175-182). IEEE Computer Society.
  • Ma, H., Schewe, K. D., & Wang, Q. (2006) "A Heuristic Approach to Cost-Efficient Fragmentation and Allocation of Complex Value Databases". In Proceedings of the Australasian Database Conference, CRPIT Vol. 49 (pp. 119-128). Australian Computer Society.
  • Ma, H., Schewe, K. D., & Kirchberg, M. (2006) "A Heuristic Approach to Vertical Fragmentation Incorporating Query Information". In Proceedings of the Baltic Database and Information Systems Conference (pp. 69-76). IEEE Computer Society.
  • Ma, H., & Schewe, K. D. (2006) "Query Optimisation as Part of Distribution Design for Complex Value Databases". In Y. Kiyoki, H. Kangassalo, H. Jaakkola, & J. Henno (Eds.), Information Modelling and Knowledge Bases, Frontiers in Artificial Intelligence and Applications (Vol. 136, pp. 289-296). IOS Press.
  • Le, P., Gao, X., & Zhang, M. (2006) "Data Extraction from Semi-structure Web Pages by Clustering". In Web Intelligence 2006: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence (pp. 374-377). Hong Kong: IEEE Press.
  • Gao, X., Zhang, M., & Cao, M. D. (2006) "Treewrapper: Automatic data extraction based on tree representation". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 4304 LNAI (pp. 566-576). doi:10.1007/11941439_61
  • Gao, X., Zhang, M., & Cao, M. D. (2006) "TreeWrapper: Automatic Data Extraction Based on Tree Representation". In Lecture Notes in Computer Science (pp. 566-576). Springer Berlin Heidelberg. doi:10.1007/11941439_61
  • Gao, X., Zhang, M., & Cao, M. (2006) "TreeWrapper: Automatic Data Extraction based on Tree Representation". In A. Sattar, & B. Kong (Eds.), Proceedings of the 19th Australian Joint Conference on Artificial Intelligence, Lecture notes in Computer Science (pp. 556-576). Berlin: Springer.
  • Gao, X., & Zhang, M. (2006) "Information Extraction from a Whole Web Site". Frontiers in Artificial Intelligence and Applications, 138, 52-57.
  • Crabtree, D., Andreae, P., & Gao, X. (2006) "Query directed web page clustering". In J. Liu, & B. Wah (Eds.), 2006 IEEE/WIC/ACM International conference on Web Intelligence (pp. 202-210). Hong Kong: IEEE. Retrieved from http://dl.acm.org/citation.
  • Gao, T., Cooper, K., Ma, H., & Yen, I. L. (2005) "Toward a UML profile to support component-based distributed adaptive systems". In 17th International Conference on Software Engineering and Knowledge Engineering, SEKE 2005 (pp. 217-222).
  • Chen, G., Yang, Z., He, H., & Goh, K. M. (2005) "Coordinating Multiple Agents via Reinforcement Learning". Autonomous Agents and Multi-Agent Systems, 10(3), 273-328. doi:10.1007/s10458-004-4344-3
  • Zhang, M., Zhang, Y., & Smart, W. (2005) "Program simplification in genetic programming for object classification". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 3683 LNAI (pp. 988-996). doi:10.1007/11553939_139
  • Zhang, M., & Smart, W. (2005) "Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification". Wellington: School of Mathematics, Statistics, and Computer Science VUW.
  • Zhang, M., & Smart, W. (2005) "Learning Weights in Genetic Programs Using Gradient Descent for Object Recognition". In Lecture Notes in Computer Science (pp. 417-427). Springer Berlin Heidelberg. doi:10.1007/978-3-540-32003-6_42
  • Zhang, M., & Smart, W. (2005) "Learning weights in genetic programs using gradient cescent for object recognition". In Applications of evolutionary computing (pp. 417-427). Berlind: Springer. doi:10.1007/978-3-540-32003-6_42
  • Zhang, M. (2005) "Mining small objects in large images using neural networks". In T. Young Lin, S. Ohsuga, C. J. Liau, X. Hu, & S. Tsumoto (Eds.), Foundation of data mining and knowledge discovery (pp. 289-314). Springer.
  • Wang, D., & Zhang, M. (2005) "A new approach to multiple class pattern classification with random matrices". Journal of Applied Mathematics and Decision Sciences, 3, 165-175.
  • Wang, D., Gao, X., & Li, G. (2005) "A new approach for detecting multivariate outliers". Communications in Statistics -- Theory and Methods, 34(8), 1857-1870.
  • Smart, W., & Zhang, M. (2005) "VGP (Victoria Genetic Programming) V1". 0 and V2.0 [Computer Software].
  • Smart, W., & Zhang, M. (2005) "Using genetic programming for multiclass classification by simultaneously solving component binary classification problems". In M. Keijer, & E. al (Eds.), Genetic programming (lecture notes in computer science) (Vol. 3447, pp. 227-239). Berlin: Springer.
  • Mei, Y., Zhang, W. Q., Jiang, G. Y., & Zhu, Y. (2005) "An approach to 3D scalable multiple description video coding with content delivery networks". In Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology (pp. 191-194). Retrieved from https://www.webofscience.
  • Mei, Y., Hua, Y. B., Swami, A., & Daneshrad, B. (2005) "Combating synchronization errors in cooperative relays". In 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5 (pp. 369-372). Retrieved from https://www.webofscience.
  • Ma, H., Schewe, K. D., & Zhao, J. (2005) "Cost Optimisation for Distributed Data Warehouses". In Proceedings of the Hawaii International Conference on System Sciences (pp. 283-292).
  • Ma, H., Schewe, K. D., & Wang, Q. (2005) "Distribution Design for Higher-Order Data Models". Palmerston North: Massey University.
  • Ma, H., Schewe, K. D., Thalheim, B., & Zhao, J. (2005) "View Integration and Cooperation in Databases, Data Warehouses and Web Information Systems". Journal on Data Semantics, 4, 213-249.
  • Ma, H., Schewe, K. D., & Thalheim, B. (2005) "Integration and Cooperation of Media Types". In Proceedings of the 4th International Conference on Information Systems Technology and its Applications, Lecture Notes in Informatics Vol. 63 (pp. 139-153).
  • Ma, H., & Schewe, K. D. (2005) "Query Optimisation as Part of Distribution Design for Complex Value Databases". In Proceedings of the 15th European-Japanese Conference on Information Modelling and Knowledge Bases (pp. 269-276). Tallinn University of Technology.
  • Ma, H., & Schewe, K. D. (2005) "Heuristic Horizontal XML Fragmentation". In Proceedings of the Conference on Advanced Information Systems Engineering (pp. 131-136). FEUP.
  • Ma, H., & Schewe, K. D. (2005) "A Heuristic Approach to Horizontal Fragmentation in Object Oriented Databases". In J. Barzdins, & A. Caplinskas (Eds.), Databases and Information Systems lll, Frontiers in Artificial Intelligence and Applications (Vol. 118, pp. 20-33). IOS Press.
  • Ma, H. (2005) "A Heuristic Approach to Cost-Efficient Fragmentation and Allocation of Complex Value Databases". Palmerston North: Massey University.
  • Fogelberg, C., & Zhang, M. (2005) "VLGP (Victoria Linear Genetic Programming package) V1". 0 [Computer Software]. VUW.
  • Fogelberg, C., & Zhang, M. (2005) "Linear genetic programming for multi-class object classification". In S. Zhang, & R. Jarvis (Eds.), AI 2005: advances in artificial intelligence (lecture notes in artificial intelligence) (Vol. 3809, pp. 369-379). Berlin: Springer. doi:10.1007/11589990_39
  • Crabtree, D., Gao, X., & Andreae, P. (2005) "Universal evaluation methods for web clustering results". In Unknown Conference (pp. 1-10). Wellington: Victoria University of Wellington, SMSCS.
  • Crabtree, D., Gao, X., & Andreae, P. (2005) "Standardized evaluation method for web clustering results". In Proceedings of the 2005 IEEE/WIC/ACM international conference on web intelligence (pp. 280-283). Compiegne: International Conference on Web Intelligence.
  • Crabtree, D., Gao, X., & Andreae, P. (2005) "Improving web clustering by cluster selection". In Proceedings of the 2005 IEEE/WIC/ACM international conference on Web Intelligence (pp. 172-178). Compiegne: International Conference on Web Intelligence.
  • Chen, G., & Zhang, M. (2005) "Evolving while-loop structures in genetic programming for factorial and ant problems". In S. Zhang, & R. Jarvis (Eds.), AI 2005: advances in artificial intelligence (lecture notes in artificial intelligence) (Vol. 3809, pp. 1079-1085). Berlin: Springer. doi:10.1007/11589990_144
  • Cao, M., & Gao, X. (2005) "Combining contents and citations for scientific document classification". In S. Zhang, & R. Jarvis (Eds.), The Proceedings of the 18th Australian joint conference on Artificial Intelligence (pp. 143-153). Berlin: Springer.
  • Zhang, M., Andreae, P., & Bhowan, U. (2004) "A two phase genetic programming approach to object detection". Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3215, 232-245.
  • GAO, X., & ZHANG, M. (2004) "A KNOWLEDGE LEARNING APPROACH TO INFORMATION EXTRACTION FROM MULTIPLE TEXT BASED WEB SITES". International Journal on Artificial Intelligence Tools, 13(03), 721-738. doi:10.1142/s0218213004001764
  • Zhao, J., & Ma, H. (2004) "Quality-Assured Design of On-Line Analytical Processing Systems using Abstract State Machines". In Proceedings of the Fourth International Conference on Quality Software (pp. 224-231). IEEE Computer Society Press.
  • Zhao, J., & Ma, H. (2004) "ASM-Based Design of Data Warehouses and On-Line Analytical Processing Systems". Palmerston North: Massey University.
  • Zhang, M., & Zhang, Y. (2004) "A New Program Structure in Genetic Programming for Object Classification". In D. Pairman, H. North, & S. McNeill (Eds.), Proceedings of the IVCNZ04 (Image and Vision Computing) International Conference (pp. 459-465). Lincoln: Landcare Research.
  • Zhang, M., & Zhang, Y. (2004) "A Multiple-Output Program Tree Structure in Genetic Programming". In R. McKay (Ed.), Proceedings of the 2004 Asia-Pacific Workshop on Genetic Programming (pp. 12). Cairns: Asia-Pacific Workshop on Genetic Programming.
  • Zhang, M., & Smart, W. (2004) "Multiclass object classification using genetic programming". In G. Raidl, & E. al (Eds.), Applications of evolutionary computing (Vol. 3005, pp. 369-378). Berlin: Springer.
  • Zhang, M., & Smart, W. (2004) "Genetic programming with gradient descent for multiclass object classification". In M. Keijer, & E. al (Eds.), Genetic programming (Vol. 3003, pp. 399-408). Berlin: Springer.
  • Zhang, M., & Ny, B. (2004) "False alarm filter in neural networks for multiclass object detection". In M. Negotia, R. Howett, & L. Jain (Eds.), Knowledge-based intelligent information and engineering systems (lecture notes in artifical intelligence: 8th international conference on knowledge based intelligent information and engineering systems) (Vol. 3214, pp. 541-548). New York: Springer.
  • Zhang, M., & Ny, B. (2004) "False Alarm Filter in Neural Networks for Multiclass Object Detection". In M. Negotia, R. Howett, & L. Jain (Eds.), Lecture Notes in Artificial Intelligence : Eighth International Conference on Knowledge Based Intelligent Information and Engineering Systems 3214 (pp. 541-548). New York: Springer.
  • Zhang, M., & Ciesielski, V. (2004) "Neural Networks and Genetic Algorithms for Domain Independent Multiclass Object Detection". International Journal on Computational Intelligence and Applications, 4(1), 77-108.
  • Zhang, M., & Bhowan, U. (2004) "Program size and pixel statistics in genetic programming for object detection". In G. Raidl, & E. al (Eds.), Applications of evolutionary computing (Vol. 3005, pp. 379-388). Berlin: Springer.
  • Xie, J., Andreae, P., Zhang, M., & Warren, P. (2004) "Learning Models for English Speech Recognition". Australian Computer Science Communications, 26(7), 323-330.
  • Xie, J., Andreae, P., Zhang, M., & Warren, P. (2004) "Learning models for English speech recognition". In Proceedings of the twenty-seventh Australasian computer science Conference (ACSC2004) Vol. 26 (pp. 323-329). Dunedin: Australasian Computer Science Conference.
  • Xie, J., Andreae, P., Zhang, M., & Warren, P. (2004) "Detecting Stress in Spoken English Using Decision Trees and Support Vector Machines". Australian Computer Science Communications, 26(7), 145-150.
  • Xie, J., Andreae, P., Zhang, M., & Warren, P. (2004) "Detecting stress in spoken English using decision trees and support vector machines". In M. Purvis (Ed.), Proceedings of Australasian workshop on Data Mining and Web Intelligence (DMWI2004) (pp. 145-150). Dunedin, New Zealand.
  • Smart, W., & Zhang, M. (2004) "Tracking Object Positions in Real-time Video using Genetic Programming". Wellington: VUW SMCS.
  • Smart, W., & Zhang, M. (2004) "Tracking Object Positions in Real-time Video using Genetic Programming". In D. Pairman, H. North, & S. McNeill (Eds.), Proceedings of the IVCNZ04 (Image and Vision Computing) International Conference (pp. 113-118). Lincoln: Landcare Research.
  • Smart, W., & Zhang, M. (2004) "Probability Based Genetic Programming for Mutliclass Object Classification". Wellington: VUW SMCS.
  • Smart, W., & Zhang, M. (2004) "Probability based genetic programming for multiclass object classification". In C. Zhang, & E. al (Eds.), PRICAI 2004: trends in artificial intelligence (pp. 251-261). Berlin: Springer. doi:10.1007/978-3-540-28633-2_28
  • Smart, W., & Zhang, M. (2004) "Probability Based Genetic Programming for Multiclass Object Classification". In H. Gregson, W. Yeap, & C. Zhang (Eds.), Lecture Notes in Computer Science: 8th Pacific Rim International Conference on Artificial Intelligence 3157 (pp. 251-261). New York: Springer.
  • Smart, W., & Zhang, M. (2004) "Multiple Object Classification Using Genetic Programming". Classical and Quantum Gravity, 3005, 367-376.
  • Smart, W., & Zhang, M. (2004) "Multiclass Object Classification Using Genetic Programming". Wellington: VUW SMCS.
  • Smart, W., & Zhang, M. (2004) "Genetic Programming with Gradient Descent for Multiclass Object Classification". Classical and Quantum Gravity, 3003, 399-408.
  • Smart, W., & Zhang, M. (2004) "Genetic Programming with Genetic Descent Search for Mulitclass Object Classification". Wellington: VUW SMCS.
  • Smart, W., & Zhang, M. (2004) "Evolving Weights in Genetic Programs Using Gradient Descent". Wellington: VUW SMCS.
  • Smart, W., & Zhang, M. (2004) "Continuously Evolving Programs in Genetic Programming Using Gradient Descent". Wellington: VUW SMCS.
  • Smart, W., & Zhang, M. (2004) "Continuously Evolving Programs in Genetic Programming Using Gradient Descent". In R. McKay (Ed.), Proceedings of the 2004 Asia-Pacific Workshop on Genetic Programming (pp. 16). Cairns: Asia-Pacific Workshop on Genetic Programming.
  • Smart, W., & Zhang, M. (2004) "Applying gradient descent search to genetic programming for object recognition". Australian Computer Science Communications, 26(7), 133-138.
  • Smart, W., & Zhang, M. (2004) "A Multiple-Output Program Tree Structure in Genetic Programming". Wellington: VUW SMCS.
  • Ny, B., & Zhang, M. (2004) "False Alarm Filters in Neural Networks for Multiclass Object Detection". Wellington: VUW SMCS.
  • Ma, H., Schewe, K. D., Thalheim, B., & Zhao, J. (2004) "View Integration and Cooperation in Databases, Data Warehouses and Web Information Systems". Palmerston North: Massey University.
  • Ma, H., & Schewe, K. D. (2004) "Query Cost Analysis for Horizontally Fragmented Complex Value Databases". In Proceedings of the 3rd Chilean Database Workshop.
  • Ma, H., & Schewe, K. D. (2004) "A Heuristic Approach to Horizontal Fragmentation in Object Oriented Databases". In Proceedings of the Baltic Database and Information Systems Conference (pp. 31-46).
  • Ma, H. (2004) "Query Cost Analysis for Horizontally Fragmented Complex Value Databases". Palmerston North: Massey University.
  • Lett, M., & Zhang, M. (2004) "New Fitness Functions in Genetic Programming for Object Detection". Wellington: VUW SMCS.
  • Lett, M., & Zhang, M. (2004) "New Fitness Functions in Genetic Programming for Object Detection". In D. Pairman, H. North, & S. McNeill (Eds.), Proceedings of the IVCNZ04 (Image and Vision Computing) International Conference (pp. 441-446). Lincoln: Landcare Research.
  • Gao, X., Zhang, M., & Andreae, P. (2004) "Automatic Pattern Construction for Web Information Extraction". International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, 12(4), 447-470.
  • Gao, X., & Zhang, M. (2004) "A Knowledge Learning Approach to Information Extraction from Multiple Text Based Web Sites". International Journal on Artificial Intelligence Tools, 13(3-4), 721-738.
  • Chen, G., Yang, Z., See, S., & Song, J. (2004) "Agent-Mediated Genetic Super-Scheduling in Grid Environments". In Lecture Notes in Computer Science (pp. 367-371). Springer Berlin Heidelberg. doi:10.1007/978-3-540-30501-9_77
  • Bhowan, U., & Zhang, M. (2004) "Program Size and Pixel Statistics in Genetic Programming for Object Detection". Applications of Evolutionary Computing, Lecture Notes in Computer Science, 3005, 377-386.
  • Bhowan, U., & Zhang, M. (2004) "Pixel Statistics and Program Size in Genetic Programming for Object Detection". Wellington: VUW SMCS.
  • Andreae, P., Collins, R., & Gao, X. (2004) "Approximately Repetitive Structure Detection for Wrapper Induction". Wellington: Victoria University of Wellington, SMCS.
  • Andreae, P., Collins, R., & Gao, X. (2004) "Approximately Repetitive Structure Detection for Wrapper Induction". In Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence Vol. 3157 (pp. 585-594). Auckland: PRICAI.
  • Andreae, P., Collins, R., & Gao, X. (2004) "A Two Phase Genetic Programming Approach to Object Detection". In L. Zhang, H. Guesgen, & W. Yeap (Eds.), Lecture Notes in Artificial Intelligence: Eighth International Conference on Knowledge Based Intelligent Information and Engineering Systems (pp. 585-594). New York: Springer.
  • Andreae, P., Bhowan, U., & Zhang, M. (2004) "A Two Phase Genetic Programming Approach to Object Detection". D. Pairman, H. North, & S. McNeill (Eds.), Wellington: VUW SMCS.
  • Andreae, P., Bhowan, U., & Zhang, M. (2004) "A two phase genetic programming approach to object detection". In M. Negotia, R. Howlett, & L. Jain (Eds.), Lecture notes in artificial intelligence: eighth international conference on knowledge based intelligent information and engineering systems (pp. 224-231).
  • Mei, Y., Le-Ngoc, T., & Lynch, W. E. (2003) "A combined multiple-candidate likelihood decoding and error concealment scheme for compressed video transmission over noisy channels". Signal Processing: Image Communication, 18(10), 971-980. doi:10.1016/j.image.2003.08.012
  • Chen, G., Yang, Z., He, H., & Goh, K. M. (2003) "Multi-agent coordination". In Proceedings of the second international joint conference on Autonomous agents and multiagent systems (pp. 960-961). ACM. doi:10.1145/860575.860745
  • Zhang, M., Ciesielski, V., & Andreae, P. (2003) "A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming". EURASIP Journal on Applied Signal Processing. Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis, 2003(8), 841-859.
  • Zhang, M., Andreae, P., & Pritchard, M. (2003) "Pixel Statistics and False Alarm Area in Genetic Programming for Object Detection". In S. Cagnoni, & E. al (Eds.), Proceedings of the European Conference on Genetic Programming for Image Analysis and Signal Processing, Applications of Evolutionaty Computing Vol. 2611 (pp. 455-466). Heidelberg, Germany: Springer.
  • Smart, W., & Zhang, M. (2003) "Classification Strategies for Image Classification in Genetic Programming". In D. Bailey (Ed.), Proceedings of the Image and Vision Computing Conference, New Zealand (pp. 402-407). Palmerston North: Institute of Information Sciences and Technology.
  • Smart, W., & Zhang, M. (2003) "Applying Online Gradient Descent Search to Genetic Programming for Object Recognition". Wellington: VUW SMCS.
  • Ma, H., Schewe, K. D., & Hartmann, S. (2003) "Distribution Design for XML Documents". In Proceedings of the Third International Conference on Electronic Commerce Engineering (pp. 1007-1012). HANGzhou, China.
  • Ma, H., & Schewe, K. D. (2003) "Fragmentation of XML Documents". In Proceedings of SBBD (pp. 200-214). Manaus, Brazil.
  • Ma, H. (2003) "Distributed Design for Object Oriented Databases". (Master's Thesis).
  • Gao, X., Zhang, M., & Andreae, P. (2003) "Learning Information Extraction Patterns from Tabular Web Pages without Manual Labelling". In J. Liu (Ed.), Proceedings of the IEEE/ WIC International Conference on Web Intelligence (pp. 495-498). Washington, D.C.: IEEE Computer Society. Retrieved from http://csdl.computer.org/comp/proceedings/wi/2003/1932/00/19320495abs.
  • Gao, X., & Zhang, M. (2003) "Learning Knowledge Bases for Information Extraction from Multiple Text Based Web Sites". Wellington: Victoria University of Wellington SMCS.
  • Gao, X., & Zhang, M. (2003) "Learning Knowledge Bases for Information Extraction from Multiple Text Based Web Sites". In J. Liu (Ed.), Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology (pp. 119-125). Washington, D.C.: IEEE Computer Society. Retrieved from http://csdl.computer.org/comp/proceedings/iat/2003/1931/00/19310119abs.
  • Gao, X., & Zhang, M. (2003) "Knowledge Discovery from Text Based Web Sites". In A. Ellis, & A. Treloar (Eds.), Proceedings of the Ninth Australian World Wide Web Conference (pp. 400-411). Gold Coast: Southern Cross University. Retrieved from http://ausweb.scu.edu.
  • Andreae, P., Xie, J., Warren, P., & Zhang, M. (2003) "Learning Models for English Speech Recognition". Wellington: VUW SMCS.
  • Andreae, P., Warren, P., Xie, J., & Zhang, M. (2003) "Detecting Stress in Spoken English using Decision Trees and Support Vector Machines". Wellington: VUW SMCS.
  • Andreae, P., Gao, X., & Zhang, M. (2003) "Learning Information Extraction Patterns from Tabular Web Pages without Manual Labelling". Wellington: Victoria University of Wellington, SMCS.
  • Andreae, P., Ciesielski, V., & Zhang, M. (2003) "A Domain Independent Window-Approach to Multiclass Object Detection Using Genetic Programming". EURASIP Journal on Applied Signal Processing, 8, 841-859.
  • Molla, M., Andreae, P., Glasner, J., Blattner, F., & Shavlik, J. (2002) "Interpreting Microarray Expression Data Using Text Annotating the Genes". In Proceedings of the Joint Conference on Information Sciences Vol. 6 (pp. 1224-1230).
  • Molla, M., Andreae, P., Glasner, J., Blattner, F., & Shavlik, J. (2002) "Interpreting microarray expression data using text annotating the genes". Information Sciences, 146(1-4), 75-88. doi:10.1016/S0020-0255(02)00216-5
  • Zhang, M., Ciesielski, V., & Andreae, P. (2002) "An independent approach to multiclass object detection using genetic programming". Wellington: VUW SMCS.
  • Zhang, M., & Ciesielski, V. (2002) "A domain independent approach to multiclass 2D object detection using neural networks and genetic algorithms". Wellington: VUW SMCS.
  • Zhang, M., Andreae, P., & Pritchard, M. (2002) "Pixel statistics and false alarm area in genetic programming". Wellington: VUW SMCS.
  • Zhang, M., Andreae, P., & Chow, R. (2002) "Pixel statistics based neural networks for domain independent multiclass object detection". Wellington: VUW SMCS.
  • Zhang, M., Andreae, P., & Chow, R. (2002) "Pixel statistics based neural networks for domain independent multiclass object detection". In D. Kenwright (Ed.), Proceedings of Image and vision computing (pp. 279-284). Auckland: Wickliffe.
  • Zhang, M. (2002) "Object mining in image data using neural networks". Communication of Information and Computing Machinery, 5(2), 107-112.
  • Zhang, M. (2002) "Neural networks for mining multiple class objects in image data". Wellington: VUW SMCS.
  • Molla, M., Andreae, P., Glasner, F., Blattner, F., & Shavlik, J. (2002) "Interpreting microarray expression data using the text annotating the Genes". Journal of Information Sciences, 146(1), 73-86.
  • Gao, X., & Pestov, V. (2002) "On a universality property of some abellian polish groups". Wellington: VUW SMCS.
  • Zhang, M. (2001) "Pixel Based Neural Networks for Multiclass Object Detection". Wellington: VUW SMCS.
  • Zhang, M. (2001) "An Overview of Speech Recognition and Related Machine Learning Techniques". Wellington: VUW SMCS.
  • Zhang, M. (2001) "A Speech Recognition System for Improving English Pronunciation of Mandarin Speakers". Wellington: VUW SMCS.
  • Zhang, M. (2001) "A neural network package for domain independent object detection [Computer Software]". Software was developed in ANSI C.
  • Zhang, M. (2001) "A Domain Independent Approach to 2D Object Detection Based on Neural and Genetic Paradigms". (PhD Thesis).
  • Wilson, P., & Zhang, M. (2001) "Object Oriented Genetic Programming (a later version is called RMITGP) [Computer Software]". Victoria University of Wellington.
  • Gao, X., & Sterling, L. (2001) "Knowledge-Based Information Agents". In R. Kowalczyk, S. Loke, N. Reed, & G. Williams (Eds.), Advances in Artificial Intelligence, Lecture Notes in Computer Science (Vol. Part III, pp. 229-238). Heidelberg, Berlin: Springer Verlag.
  • Gao, X. (2001) "Knowledge-based Information Extraction from the World Wide". (PhD Thesis).
  • Gao, X., & Sterling, L. (2000) "Semi-structured data extraction from heterogeneous sources". In D. Schwartz, M. Divitini, & T. Brasethvik (Eds.), Internet-based organisational memory and knowledge management (pp. 83-102). Hershey, PA: The Idea Group Publisher.
  • Gao, X., & Sterling, L. (2000) "Knowledge-based Information Agents". In F. Mizoguchi, L. Sterling, & S. Loke (Eds.), Proceedings of the 1st Pacific Rim International Workshop on Intelligent Information Agents (pp. 48-58). Melbourne: Deakin University.
  • Gao, X., & Sterling, L. (2000) "AutoWrapper: automatic wrapper generation for multiple online services". In G. Young (Ed.), World wide web - technologies and applications for the new millennium (pp. 61-70). Las Vegas: US CSREA Publisher.
  • Andreae, P., Biddle, R., Dobbie, G., Gale, A., Miller, I., & Tempero, E. (2000) "Experience teaching CS1 with Java". Journal of Computer Science Education, 14(1-2), 19-28.
  • Zhang, M., & Ciesielski, V. (1999) "Using back propagation algorithm and genetic algorithm to train and refine neural networks for object detection". In Unknown Book (Vol. 1677, pp. 626-635). doi:10.1007/3-540-48309-8_58
  • Zhang, M., & Ciesielski, V. (1999) "Genetic programming for multiple class object detection". In Unknown Book (Vol. 1747, pp. 180-192). doi:10.1007/3-540-46695-9_16
  • Dobbie, G., & Andreae, P. (1999) "Problems with OOFNF: a proposed normal form for object oriented databases". Wellington: VUW SMCS.
  • Andreae, P., Biddle, R., Dobbie, G., Gale, A., Miller, I., & Tempero, E. (1999) "Experience teaching CS1 with Java". Wellington: VUW SMCS.
  • Hall, P., Ngan, M., & Andreae, P. (1998) "Reconstructing vascular skeletons from x-ray angiograms". In Proceedings of SPIE - The International Society for Optical Engineering Vol. 3338 (pp. 480-491). doi:10.1117/12.310927
  • Raman, A., Andreae, P., & Patrick, J. (1998) "A beam search algorithm for PFSA inference". Pattern Analysis and Applications, 1(2), 121-129. doi:10.1007/BF01237940
  • King-Jet Tseng., & Chen, G. H. (1997) "Computer-aided design and analysis of direct-driven wheel motor drive". IEEE Transactions on Power Electronics, 12(3), 517-527. doi:10.1109/63.575679
  • Brown, J., Andreae, P., Biddle, R., & Tempero, E. (1997) "Women in introductory computer science". In Proceedings of the twenty-eighth SIGCSE technical symposium on Computer science education (pp. 111-115). ACM. doi:10.1145/268084.268128
  • Hall, P., Ngan, M., & Andreae, P. (1997) "Reconstruction of vascular networks using three-dimensional models". IEEE Transactions on Medical Imaging, 16(6), 919-929. doi:10.1109/42.650888
  • Brown, J., Andreae, P., Biddle, R., & Tempero, E. (1997) "Women in introductory computer science: Experience at Victoria University of Wellington". SIGCSE Bulletin (Association for Computing Machinery, Special Interest Group on Computer Science Education), 29(1), 111-115. doi:10.1145/268085.268128
  • Hall, P., Ngan, M., & Andreae, P. (1996) "Reconstruction of blood vessel networks from x-ray projections and a vascular catalogue". In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 1065 (pp. 293-303). doi:10.1007/3-540-61123-1_148
  • Hall, P., Andreae, P., & Ngan, M. (1995) "Reconstruction of blood vessel networks from a few perspective projections". In Proceedings - 1995 2nd New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, ANNES 1995 (pp. 369-372). doi:10.1109/ANNES.1995.499510
  • Dawkins, B. P., Andreae, P. M., & O’Connor, P. M. (1994) "Analysis of olympic heptathlon data". Journal of the American Statistical Association, 89(427), 1100-1106. doi:10.1080/01621459.1994.10476848
  • Andreae, J. H., Ryan, S. W., Tomlinson, M. L., & Andreae, P. M. (1993) "Structure from associative learning". International Journal of Man-Machine Studies, 39(6), 1031-1050. doi:10.1006/imms.1993.1094
  • Andreae, P. (1987) "Constraint Limited Generalization: Acquiring Procedures from Examples". In Matters of Intelligence (pp. 311-331). Springer Netherlands. doi:10.1007/978-94-009-3833-5_14
  • Finlay, J. B., Bourne, R. B., Andreae, P., & Papadopoulos, P. V. (1984) "TIBIAL STRAINS IN THE OSTEOARTHRITIC KNEE IN VITRO: VARUS/VALGUS LOADING". In Unknown Conference (pp. 71-72).
  • Finlay, J. B., Bourne, R. B., Andreae, P., & Landsberg, R. P. (1984) "ROLE OF THE SUBCHONDRAL BONE-PLATE IN THE LONGEVITY OF ACETABULAR PROSTHESES". In Unknown Conference (pp. 73-74).
  • Andreae, P. M. (1983) "CONSTRAINT LIMITED GENERALIZATION: ACQUIRING PROCEDURES FROM EXAMPLES". In Unknown Conference (pp. 6-10).
  • Andreae, J. H., & Andreae, P. M. (1979) "MACHINE LEARNING WITH A MULTIPLE CONTEXT". Proceedings - International Conference on Cybernetics and Society, 734-739.
  • Andreae, P. M., & Andreae, J. H. (1978) "A teachable machine in the real world". International Journal of Man-Machine Studies, 10(3), 301-312. doi:10.1016/S0020-7373(78)80048-0
  • Zhang, Y., Zhang, F., Casanovas, P., Xue, B., Zhang, M., & Symonds, J. E. (2024) "A study of feature importance for king salmon health classification with feature selection". Intelligent Marine Technology and Systems, 2(1). doi:10.1007/s44295-024-00046-z
  • Zhang, R., Lensen, A., & Sun, Y. (n.d.) "Speeding up Genetic Programming Based Symbolic Regression Using GPUs". __.
  • Zhang, M., & Fogelberg, C. G. (n.d.) "Genetic Programming for Image Recognition: An LGP Approach". In Lecture Notes in Computer Science (pp. 340-350). Springer Berlin Heidelberg. doi:10.1007/978-3-540-71805-5_37
  • Zhang, F., Shi, G., & Mei, Y. (n.d.) "Interpretability-aware multi-objective genetic programming for scheduling heuristics learning in dynamic flexible job shop scheduling". In IEEE Congress on Evolutionary Computation (CEC).
  • Zhang, F., Mei, Y., & Zhang, M. (n.d.) "An investigation of terminal settings on multitask multi-objective dynamic flexible job shop scheduling with genetic programming". In ACM Genetic and Evolutionary Computation Conference (GECCO).
  • Zhang, F. (n.d.) "Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling". Retrieved from https://doi.org/10.26686/wgtn.
  • Zeng, P., Song, X., Lensen, A., Ou, Y., Sun, Y., Zhang, M., & Lv, J. (2023) "Differentiable Genetic Programming for High-dimensional Symbolic Regression". __.
  • Yan Mei., Lynch, W. E., & Tho Le-Ngoc. (n.d.) "Joint forward error correction and error concealment for compressed video". In Proceedings. International Conference on Information Technology: Coding and Computing (pp. 410-415). IEEE Comput. Soc. doi:10.1109/itcc.2002.1000424
  • Xue, B., & Zhang, M. (2016) "Evolutionary Computation for Feature Manipulation in Machine Learning and Data Mining". In Proceedings of 2016 IEEE World Congress on Computational Intelligence/ IEEE Congress on Evolutionary Computation (WCCI 2016 /CEC2016) (pp. 3061-3067). Vancouver.
  • Xu, M., Mei, Y., Zhang, F., & Zhang, M. (n.d.) "Multi-objective genetic programming based on decomposition on evolving scheduling heuristics for dynamic scheduling". In ACM Genetic and Evolutionary Computation Conference (GECCO).
  • Wang, P., Xue, B., Liang, J., & Zhang, M. (2024) "Genetic Programming for Automatically Evolving Multiple Features to Classification". Evolutionary Computation, 1-28. doi:10.1162/evco_a_00359
  • Wang, F., Yuan, X., Zhou, L., Liu, S., Zhang, M., & Zhang, D. (2021) "Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China". Remote Sensing, 13(21), 4248. doi:10.3390/rs13214248
  • Tran, T., Zhang, M., Andreae, P., & Xue, B. (n.d.) "A Wrapper Feature Selection Approach to Classification with Missing Data". In Proceedings of the 19th European Conference on the Applications of Evolutionary Computation (EvoIASP’16).
  • Tan, B., Ma, H., Mei, Y., & Zhang, M. (n.d.) "Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem". IEEE Transactions on Services Computing. doi:10.1109/TSC.2018.2793266
  • Smart, W., Andreae, P., & Zhang, M. (n.d.) "Empirical Analysis of GP Tree-Fragments". In Lecture Notes in Computer Science (pp. 55-67). Springer Berlin Heidelberg. doi:10.1007/978-3-540-71605-1_6
  • Qurrat Ul Ain. (n.d.) "Genetic Programming based Feature Manipulation for Skin Cancer Image Classification". Retrieved from https://doi.org/10.26686/wgtn.
  • Pei, J., Tong, H., Liu, J., Mei, Y., & Yao, X. (n.d.) "Local optima correlation assisted adaptive operator selection". In ACM Genetic and Evolutionary Computation Conference (GECCO).
  • Patterson, G., & Zhang, M. (n.d.) "Fitness Functions in Genetic Programming for Classification with Unbalanced Data". In Lecture Notes in Computer Science (pp. 769-775). Springer Berlin Heidelberg. doi:10.1007/978-3-540-76928-6_90
  • Nguyen, S., Mei, Y., Xue, B., & Zhang, M. (n.d.) "A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules". Evolutionary Computation. doi:10.1162/evco_a_00230
  • Nguyen, B. H., Xue, B., & Zhang, M. (2024) "Evolutionary Sparsity Regularisation-Based Feature Selection for Binary Classification". Evolutionary Computation, 1-34. doi:10.1162/evco_a_00358
  • MOSTOFI, A., JAIN, V., & MEI, Y. I. (2020) "A Model for Pricing the License of Brand-Name Drugs Using Cooperative Game Theory". In International Joint Conference on Industrial Engineering and Operations Management Proceedings. IJCIEOM 2020 - International Joint Conference on Industrial Engineering and Operations Management. doi:10.14488/ijcieom2020_full_0012_37191
  • Mirghasemi, S., Andreae, P., Zhang, M., & Rayudu, R. (2017) "Severely noisy image segmentation via wavelet shrinkage using PSO and Fuzzy C-Means". In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (pp. 1663-1670). Athens, Greece. doi:10.1109/SSCI.2016.7850051
  • Mei, Y., Chen, Q., Lensen, A., Xue, B., & Zhang, M. (n.d.) "Explainable Artificial Intelligence by Genetic Programming: A Survey". IEEE Transactions on Evolutionary Computation.
  • MacLachlan, J., Mei, Y., Zhang, F., Zhang, M., & Signal, J. (n.d.) "Learning emergency medical dispatch policies via genetic programming". In ACM Genetic and Evolutionary Computation Conference (GECCO).
  • Liang, Y., Zhang, M., & Browne, W. (2016) "Feature Construction using Genetic Programming for Figure-ground Image Segmentation". In Proceedings of the 20th Asia-Pacific Symposium on Intelligent and Evolutionary Systems. Canberra.
  • Lensen, A. (n.d.) "I routinely make all my code publicly available for research dissemination purposes, with over nine separate codebases hosted at https://github". com/AndLen and on publisher websites. [Computer Software].
  • KAS, K. N., Hijazi, M. H. A., Chen, G., & Sarrafzadeh, A. (2015) "A Review: Software Defined Networks Management". Proceedings of the Asia-Pacific Advanced Network, 39(0), 20. doi:10.7125/apan.39.2
  • Karunakaran, D., Mei, Y., Chen, G., & Zhang, M. (2016) "Dynamic Job Shop Scheduling Under Uncertainty Using Genetic Programming". In Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium (pp. 195-210). Canberra, Australia: Springer International Publishing. doi:10.1007/978-3-319-49049-6_14
  • Jiao, R., Xue, B., & Zhang, M. (2023) "A Tri-objective Method for Bi-objective Feature Selection in Classification". Evolutionary Computation, 1-30. doi:10.1162/evco_a_00339
  • Iqbal, M., Zhang, M., & Xue, B. (2016) "Transfer Learning in Genetic Programming for Image Classification". In Proceedings of 2016 IEEE World Congress on Computational Intelligence/ IEEE Congress on Evolutionary Computation (WCCI 2016 /CEC2016) (pp. 3582-3589). Vancouver.
  • Huang, Z., Mei, Y., Zhang, F., & Zhang, M. (n.d.) "Grammar-guided linear genetic programming for dynamic job shop scheduling". In ACM Genetic and Evolutionary Computation Conference (GECCO).
  • Huang, V., Fu, Q., Chen, G., Wen, E., & Hart, J. (2017) "BLAC: A Bindingless Architecture for Distributed SDN Controllers". In 2017 IEEE 42nd Conference on Local Computer Networks (LCN) (pp. 146-154). Singapore. doi:10.1109/LCN.2017.62
  • Ghifary, M., Kleijn, W., Zhang, M., Balduzzi, D., & Li, W. (2016) "Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation". In Proceedings of the 14th European Conference on Computer Vision (ECCV 2016). Amsterdam, The Netherlands.
  • Gang Chen., Zhonghua Yang., Hao He., & Kiah Mok Goh. (n.d.) "Coordinating multi-agents using JavaSpaces". In Ninth International Conference on Parallel and Distributed Systems, 2002. Proceedings. (pp. 63-68). IEEE Comput. Soc. doi:10.1109/icpads.2002.1183379
  • Dai, S., Jia, Y. -H., Chen, W. -N., & Mei, Y. (n.d.) "Adaptive particle swarm optimization with local search for multi-robot multi-point dynamic aggregation". In ACM Genetic and Evolutionary Computation Conference (GECCO).
  • Costa, J., Mei, Y., & Zhang, M. (n.d.) "Learning to select initialisation heuristic for vehicle routing problems". In ACM Genetic and Evolutionary Computation Conference (GECCO).
  • Chen, Q., Zhang, M., & Xue, B. (2016) "Genetic Programming with Embedded Feature Construction for High-Dimensional Symbolic Regression". In Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization.
  • Chen, G. H., & Tseng, K. J. (n.d.) "Design of wheel motor using Maxwell 2D simulation". In Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95 Vol. 2 (pp. 634-639). IEEE. doi:10.1109/empd.1995.500801
  • Chen, G. H., & Tseng, K. J. (n.d.) "Design of a permanent-magnet direct-driven wheel motor drive for electric vehicle". In PESC Record. 27th Annual IEEE Power Electronics Specialists Conference Vol. 2 (pp. 1933-1939). IEEE. doi:10.1109/pesc.1996.548845
  • Bguyen, H. B., Zhang, M., & Xue, B. (2016) "Similarity based Multi-objective Particle Swarm Optimisation for Feature Selection in Classification". In Proceedings of the Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016) (pp. 298-310). Canberra.
  • Balduzzi, D., Ghifary, M., Kleijn, B., & Zhang, M. (n.d.) "Fast Domain Adaptation via Scatter Component Analysis". __.
  • Andersen, H., Lensen, A., Browne, W., & Mei, Y. (n.d.) "Producing diverse rashomon sets of counterfactual explanations with niching particle swarm optimization algorithms". In ACM Genetic and Evolutionary Computation Conference (GECCO).
  • Alvarez, I. M., Nguyen, T. B., Browne, W. N., & Zhang, M. (2024) "A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems". Evolutionary Computation, 1-26. doi:10.1162/evco_a_00351
  • Al-Helali, B., Chen, Q., Xue, B., & Zhang, M. (2024) "Genetic Programming-based Feature Selection for Symbolic Regression on Incomplete Data". Evolutionary Computation, 1-27. doi:10.1162/evco_a_00362
  • Rayudu, R., Xue, B., Al-Sahaf, H., Chalmers, A., Mei, Y., Lensen, A., & Chen, Q. (Eds.) (n.d.) "Proceedings of 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ 2020)". Wellington, New Zealand: IEEE. doi:10.1109/IVCNZ51579.2020
Print version | Backlinks |