Title: Recent Advances in Evolutionary Transfer Optimization
Abstract: It is known that the processes of learning and the transfer of what has been learned are central to humans in problem-solving. However, 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. I will first present an overview of existing ETO approaches for problem-solving in evolutionary computation. I will then introduce some of our recent work on evolutionary multitasking. It will end with a discussion on future ETO research directions, covering various topics ranging from theoretical analysis to real-world applications.
Biography: Kay Chen Tan is currently a Chair Professor (Computational Intelligence) of the Department of Computing, The Hong Kong Polytechnic University. He has co-authored 7 books and published over 200 peer-reviewed journal articles. Prof. Tan is currently the Vice-President (Publications) of IEEE Computational Intelligence Society, USA. He was the Editor-in-Chief of IEEE Transactions on Evolutionary Computation from 2015-2020 (IF: 11.554) and IEEE Computational Intelligence Magazine from 2010-2013 (IF: 11.356). Prof. Tan is an IEEE Fellow, an IEEE Distinguished Lecturer Program (DLP) speaker since 2012, and an Honorary Professor at University of Nottingham in UK. He is also the Chief Co-Editor of Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications since 2020.