報告人： Kay Chen Tan，香港城市大學，教授，IEEE Fellow。
報告時間：2020年12月14日 (星期) 下午3:00 - 5:00
Meeting ID: 281 001 9605
報告摘要：It is known that the processes of learning and the transfer of what has been learned are central to humans in problem-solving. Particularly, the study of optimization methodology which learns from the problem solved and transfer what have been learned to help problem-solving on unseen problems, has been under-explored in the context of evolutionary computation. This talk will touch upon the topic of evolutionary transfer optimization (ETO), which focuses on knowledge learning and transfer across problems for enhanced evolutionary optimization performance. In particular, I will first present an overview of existing ETO approaches for problem-solving in evolutionary computation. I will then introduce our work on ETO for evolutionary multitasking and dynamic multi-objective optimization. I will end my talk with a discussion of future ETO research directions.
報告人簡介：Kay Chen Tan is currently a Professor with the Department of Computer Science, City University of Hong Kong. He is currently the Editor-in-Chief of IEEE Transactions on Evolutionary Computation (IF: 11.169). He was the Editor-in-Chief of IEEE Computational Intelligence Magazine from 2010-2013 (IF: 9.083). Prof. Tan currently serves as an Associate Editor for over 10 international journals, such as IEEE Transactions on AI, IEEE Transactions on Cybernetics, and IEEE Transactions on Games etc. Prof. Tan is an IEEE Fellow, IEEE Distinguished Lecturer Program (DLP) speaker since 2012, and elected member of IEEE CIS AdCom from 2014-2019.
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