Yi Mei

Victoria University Wellington, School of Engineering and Computer Science, Faculty Member

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Dr. Yi Mei is a Senior Lecturer at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. He received his BSc and PhD degrees from University of Science and Technology of China in 2005 and 2010, respectively. His research interests include evolutionary computation in scheduling, routing and combinatorial optimisation, as well as evolutionary machine learning, hyper-heuristics, genetic programming, feature selection and dimensional reduction.
Yi has more than 70 fully referred publications, including the top journals in EC and Operations Research (OR) such as IEEE TEVC, IEEE Transactions on Cybernetics, European Journal of Operational Research, ACM Transactions on Mathematical Software, and top EC conferences (GECCO). He won an IEEE Transactions on Evolutionary Computation Outstanding Paper Award 2017, and a Victoria University of Wellington Early Research Excellence Award 2018. As the sole investigator, he won the 2nd prize of the Competition at IEEE WCCI 2014: Optimisation of Problems with Multiple Interdependent Components. He serves as a Vice-Chair of the IEEE CIS Emergent Technologies Technical Committee, and a member of three IEEE CIS Task Forces and two IEEE CIS Technical Committees. He is an Editorial Board Member of International Journal of Bio-Inspired Computation, an Associate Editor of International Journal of Applied Evolutionary Computation, and a guest editor of a special issue of the Genetic Programming Evolvable Machine journal. He has organised a number of special sessions in international conferences such as IEEE CEC. He serves as a reviewer of over 30 international journals including the top journals in EC and OR. He was an Outstanding Reviewer for Applied Soft Computing in 2015 and 2017, and IEEE Transactions on Cybernetics in 2018.
Supervisors: Xin Yao
Phone: +64 4 886 5331
Address: CO353, Cotton Building, Gate 7, Kelburn Parade, Wellington, 6012

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Multitask Genetic Programming Based Generative Hyper-heuristics: A Case Study in Dynamic Scheduling

IEEE Transactions on Cybernetics, 2021

Evolutionary multitask learning has achieved great success due to its ability to handle multiple ... more Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyper-heuristic domain which aims at generating a heuristic for a class of problems rather than solving one specific problem. The existing multitask hyperheuristic studies only focus on heuristic selection, which is not applicable to heuristic generation. To fill the gap, we propose a novel multitask generative hyper-heuristic approach based on genetic programming in this paper. Specifically, we introduce the idea in evolutionary multitask learning to genetic programming hyper-heuristics with a suitable evolutionary framework and individual selection pressure. In addition, an origin-based offspring reservation strategy is developed to maintain the quality of individuals for each task. To verify the effectiveness of the proposed approach, comprehensive empirical studies have been conducted on the homogeneous and heterogeneous multitask dynamic flexible job shop scheduling. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for each task in all the examined scenarios. In addition, the evolved scheduling heuristics verify the mutual help among the tasks in a multitask scenario.

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