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Prof. Yi Mei
Professor at Victoria University of Wellington, researching evolutionary computation, genetic programming, machine learning and combinatorial optimisation.
Evolutionary Computation for Combinatorial Optimisation
Many of society’s logistics problems — collecting waste, gritting roads, delivering parcels, allocating cloud resources — are combinatorial optimisation problems: the number of possible solutions explodes with problem size. I design evolutionary and memetic algorithms that find near-optimal solution
Genetic Programming for Real-time Optimisation and Decision-Making
Real-world systems change constantly: jobs arrive, machines break down, demands and travel times are uncertain. Re-optimising after every change is too slow. My research uses genetic programming (GP) to automatically learn decision rules that make high-quality decisions in real time — in microsecond
Machine Learning and Automated Algorithm Design for Optimisation
Designing a good optimisation algorithm usually takes experts years of trial and error, and the result rarely transfers to new problems. My research uses machine learning to automate this: learning which search operator to apply, where to search, how to start, and ultimately generating whole algorit
Transfer, Multitask, and Lifelong Machine Learning
Problems rarely come alone. A factory faces many related scheduling scenarios; a logistics company solves new variants of routing every day. Instead of learning each from scratch, my research reuses knowledge — through transfer learning, multitask learning and lifelong learning — so that solvers lea
Explainable AI
AI is increasingly trusted with consequential decisions, from scheduling hospital resources to detecting cyber attacks. People need to understand and trust those decisions. My research makes AI explainable in two ways: building models that are interpretable by design, and explaining the decisions of
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