Prof. Yi Mei
I build AI solutions to learn how to make hard decisions, by evolving interpretable algorithms and policies for scheduling, routing, and resource allocation, and their applications to the real world.
Professor
Programme Director (Computer Science and Computer Graphics)
School of Engineering and Computer Science (SECS)
Centre for Data Science and Artificial Intelligence (CDSAI)
Victoria University of Wellington (VUW)
Evolutionary Computation for Combinatorial Optimisation Group (ECCO)
Evolutionary Computation and Machine Learning Research Group (ECRG)
IEEE CIS Taskforce on Evolutionary Scheduling and Combinatorial Optimisation (TESCO)
Research Themes
Evolutionary Computation for Combinatorial Optimisation
Designing evolutionary and memetic algorithms that find high-quality solutions to hard routing, scheduling and allocation problems — and scaling them to very large instances by decomposition.
Contributions: MAENS and decomposition-based memetic algorithms for arc routing; differential grouping and cooperative co-evolution for large-scale optimisation (IEEE TEVC Outstanding Paper Award).
78 papers · Explore this theme →Genetic Programming for Real-time Optimisation and Decision-Making
Evolving compact, interpretable decision rules with genetic programming that react instantly to dynamic events in factories, vehicle fleets, cloud data centres and traffic networks.
Contributions: GP hyper-heuristics for dynamic scheduling and routing; the first book on the topic; EuroGP and GECCO Best Paper Awards; HUMIES Silver Award.
167 papers · Explore this theme →Machine Learning and Automated Algorithm Design for Optimisation
Using machine learning to make optimisers smarter — learning which operator to apply, where to search, and even how to design the algorithm itself.
Contributions: Adaptive operator selection; learned and neural neighbourhood search for routing; GP-designed algorithm components; IEEE CIM Outstanding Paper Award; EvoStar 2026 Best Student Paper.
112 papers · Explore this theme →Explainable AI
Making AI decisions understandable — by evolving models that are interpretable by design, and by explaining the decisions of black-box models.
Contributions: Survey on explainable AI by GP; local and counterfactual explanations for evolved policies; interpretability-aware GP; GECCO 2022 Best Paper Award; ITNAC 2025 Best Student Paper.
18 papers · Explore this theme →Transfer, Multitask, and Lifelong Machine Learning
Reusing knowledge across related problems so that learning is faster and generalises better — from one task to another, across tasks at once, and over a lifetime of tasks.
Contributions: Knowledge-transfer and multitask GP; lifelong learning for optimiser generation and neural routing solvers (IJCAI 2025, ICLR 2026); EuroGP 2022 Best Paper Award.
34 papers · Explore this theme →News
- 2026 Award Meng Xu's paper "Genetic Programming and Reinforcement Learning on Learning Heuristics for Dynamic Scheduling: A Preliminary Comparison" won the Outstanding Paper Award of IEEE Computational Intelligence Magazine.
- 2026 Award Saining Liu's paper "Adaptive Curriculum Learning in Genetic Programming–Guided Local Search for Large-Scale Vehicle Routing Problems" won the Best Student Paper Award at EvoStar 2026.
- 2026 Recognition Recognised as an Outstanding Associate Editor of IEEE Transactions on Evolutionary Computation for the third consecutive year (2024–2026).
- 2025 Award Dedy Hendro's paper "An Investigation of Feature-Grouping for Robust SHAP Explanations in IoT Intrusion Detection" won the Best Student Paper Award at the IEEE International Telecommunication Networks and Applications Conference (ITNAC) 2025.
- 2025 Award Honoured to receive the Victoria University of Wellington Ki te Pae Research Excellence Award.
Prospective students: I am recruiting PhD, MSc, Honours and Summer Research students. If your interests align with my research themes, please email me with your CV and transcripts.
中国国家留学基金委（CSC）联合培养奖学金：请点击这里了解详情。
Biography
Yi Mei is a Professor at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.
His research interests include evolutionary computation for combinatorial optimisation, genetic programming, automatic algorithm design, explainable AI, multi-objective optimisation, transfer/multitask learning and optimisation. He has published in top journals in EC and Operations Research (OR) such as IEEE TEVC, IEEE TCYB, EJOR, IEEE Transactions on Services Computing, and ACM Transactions on Mathematical Software. He won an IEEE Transactions on Evolutionary Computation Outstanding Paper Award 2017, GECCO Best Paper Awards in 2022, 2023, 2024, the EuroGP Best Paper Award 2022, and the EvoStar Best Student Paper Award 2026.
He is an Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Computational Intelligence Magazine, IEEE Transactions on Artificial Intelligence, IEEE Computational Intelligence Letters, Journal of Scheduling, and an Editorial Board Member/Associate Editor of four other international journals. He serves as the Chair of IEEE CIS Evolutionary Computation Technical Committee and Education Portal Subcommittee. He is the Chair of IEEE New Zealand Council. He was the Chair of the IEEE CIS Taskforce on Evolutionary Scheduling and Combinatorial Optimisation. He was the Chair of the IEEE CIS Travel Grant Subcommittee, a Vice-Chair of the IEEE CIS Emergent Technologies Technical Committee, a member of three IEEE CIS Task Forces and two IEEE CIS Technical Committees. He has been recognised as one of the Stanford University/Elsevier World’s Top 2% Scientists since 2021. He is a Fellow of Engineering New Zealand and an IEEE Senior Member.
Education
- PhD in Computer Science, University of Science and Technology of China, 2010
- MSc in Computer Science, University of Science and Technology of China, 2007
- BSc in Mathematics, University of Science and Technology of China, 2005