Evolutionary Computation for Combinatorial Optimisation
(ECCO) Research Group
News
- Our group received an MBIE Smart Idea Fund "Machine Learning for Emergency Medical Dispatch: A Data Driven Approach", $1,000,000 for 3 years. This project is in collaboration with Wellington Free Ambulance.
- Our group received a Royal Society Catalyst Leaders Fund "Machine Learning for Combinatorial Optimisation: An Evolutionary Computation Approach", $150,000, 2024-2026.
- Congratulations to Zhixing Huang for winning the GP track Best Paper Award at the ACM Genetic and Evolutionary Computation Conference (GECCO) 2023 for their paper “Grammar-guided Linear Genetic Programming for Dynamic Job Shop Scheduling”.
- Congratulations to Jordan MacLachlan for winning the GECCO Humies Silver Award 2023 ($2,000 USD), with his work on genetic programming for emergency medical dispatch.
- Congratulations to Jordan MacLachlan for winning the VUW 3-minutes Thesis Presentation Award Second Prize 2023 ($1,000 NZD).
- A/Prof Yi Mei was elevated to a Fellow of Engineering New Zealand.
- Congratulations to Dr Fangfang Zhang for receiving a ACM SIGEVO Best PhD Dissertation Award (Honorable Mention/Runner-up, website) in 2022. Her thesis is “Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling”.
- Congratulations to Shaolin Wang for winning the ECOM track Best Paper Award at the ACM Genetic and Evolutionary Computation Conference (GECCO) 2022 for their paper “Local Ranking Explanation for Genetic Programming Evolved Routing Policies for Uncertain Capacitated Arc Routing Problems”.
- Congratulations to Zhixing Huang for winning the Best Paper Award at the European Conference on Genetic Programming (EuroGP) 2022 for their paper “An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling”.
Welcome to the Evolutionary Computation for Combinatorial Optimisation (ECCO) Research Group, a sub-group of the
ECRG group. Our group has a wide range of research interests and projects in solving combinatorial optimisation problems using evolutionary learning and optimisation.
Research Grants
