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Yi Mei
Researcher at Victoria University of Wellington
Publications - 191
Citations - 4704
Yi Mei is an academic researcher from Victoria University of Wellington. The author has contributed to research in topics: Genetic programming & Computer science. The author has an hindex of 26, co-authored 148 publications receiving 2998 citations. Previous affiliations of Yi Mei include RMIT University & University of Science and Technology of China.
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Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
TL;DR: An automatic decomposition strategy called differential grouping is proposed that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum and greatly improve the solution quality on large-scale global optimization problems.
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DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization
TL;DR: The proposed improved variant of the differential grouping (DG) algorithm, DG2, finds a reliable threshold value by estimating the magnitude of roundoff errors and automatic calculation of its threshold parameter, which makes it parameter-free.
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Decomposition-Based Memetic Algorithm for Multiobjective Capacitated Arc Routing Problem
TL;DR: A new memetic algorithm (MA) called decomposition-based MA with extended neighborhood search (D-MAENS) is proposed, which combines the advanced features from both the MAENS approach for single-objective CARP and multiobjective evolutionary optimization.
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A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization
TL;DR: A competitive divide-and-conquer algorithm for solving large-scale black-box optimization problems for which there are thousands of decision variables and the algebraic models of the problems are unavailable and the competitive performance of the well-known CMA-ES is extended from low-dimensional to high-dimensional black-boxes problems.
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Memetic Algorithm With Extended Neighborhood Search for Capacitated Arc Routing Problems
TL;DR: Experimental results show that MAENS is superior to a number of state-of-the-art algorithms, and the advanced performance ofMAENS is mainly due to the MS operator, which is capable of searching using large step sizes and is less likely to be trapped in local optima.