Kay Chen Tan is currently a Chair Professor (Computational Intelligence) and Associate Head (Research and Developments) of the Department of Computing, The Hong Kong Polytechnic University. He has co-authored 7 books and published over 230 peer-reviewed journal papers. 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, and an Honorary Professor at the University of Nottingham in UK. He also serves as the Chief Co-Editor of Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications.
It is known that the processes of learning and transfer of what has been learned are important to humans for solving complex problems. However, the study on optimization methodologies via learning from existing solutions and the transfer of what has been learned to help on solving related or unseen problems, has been under-explored in the context of evolutionary computation. This talk will give an overview of evolutionary transfer optimization (ETO), which is an emerging research direction that integrates evolutionary algorithm solvers with knowledge learning and transfer across different problem domains to achieve better optimization efficiency and performance. It will present some recent research work in ETO for solving multi-objective and large-scale optimization problems via high-performance computing. Some discussions on future ETO research directions, including topics such as theoretical analysis and real-world applications, will also be given.