School of Engineering and Computer Science Te Kura Mātai Pūkaha, Pūrorohiko

  • 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
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  • 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
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  • 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
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  • 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
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  • 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.
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  • 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
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  • 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.
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  • 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.
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  • 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
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  • 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
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  • 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.
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