报告题目：Evolutionary Transfer Optimization
主讲人：Kay Chen Tan，Professor，Department of Computer Science，City University of Hong Kong
Within the context of computational intelligence, several core learning technologies in neural and cognitive systems, fuzzy systems, probabilistic reasoning have been notable for their ability in emulating some of human’s cultural and generalization capabilities. In spite of the accomplishments made in computational intelligence, the attempts to emulate the cultural intelligence of human in search, evolutionary optimization in particular, have to date received less attention. 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, Prof. Tan will first present an overview of existing ETO approaches for problem-solving in evolutionary computation. He will then introduce his work on ETO for evolutionary multitasking and solving dynamic multi-objective optimization problems. He will end his talk with a discussion of future ETO research directions covering various topics ranging from theoretical analysis to real-world complex applications.
Kay Chen Tan is currently a Professor with the Department of Computer Science, City University of Hong Kong. His research interests include computational intelligence and its applications, such as in data analytics, healthcare, evolutionary transfer learning, and multi-objective optimization. Prof. Tan has co-authored 7 books and published over 300 peer-reviewed articles (including over 80 articles in IEEE Transactions). He holds one U.S. patent on surface defect detection, and another one is pending approval. His current h-index is 61 according to Google Scholar citations.
Prof. Tan is currently the Editor-in-Chief of IEEE Transactions on Evolutionary Computation (IF: 11.169). He was also 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 has received a number of research awards, such as the 2019 IEEE Computational Intelligence Magazine Outstanding Paper Awards, the 2016 IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Awards, the 2012 Outstanding Early Career Award presented by the IEEE Computational Intelligence Society, and the 2008 Recognition Award given by the International Network for Engineering Education & Research.
Prof. Tan has been invited as a Plenary/Keynote speaker for over 70 international conferences, including the 2020 IEEE World Congress on Computational Intelligence (WCCI), the 2016 IEEE Symposium Series on Computational Intelligence etc. He has served as an organizing committee Chair/Co-Chair for over 50 international conferences, such as the General Chair of 2019 IEEE Congress on Evolutionary Computation (CEC), and the General Chair of 2016 IEEE World Congress on Computational Intelligence.
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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