Differential Evolution-based Methods for Numerical Optimiza
报告题目： Differential Evolution-based Methods for Numerical Optimization
报告人：Kay Chen TAN, Department of Computer Science, City University of Hong Kong, Hong Kong, China.
摘要：Differential Evolution (DE) is arguably one of the powerful 1netaheuristics for solving numerical optimization proble1ns. Although considerable research has been devoted to the develop1nent and in1prove1nent of DE, there exist several open issues. This talk will discuss our recent works on designing ne\.v DE operators and a algorithms to overcome limitations of existing approaches in handling single and multi-objective opti1nization problems. Application of the proposed DE for solving difficult minimax optimization problems will also be presented.
个人简介：Kay Chen TAN (SM’08-F’14) received the B.Eng. (First Class Hons.) degree in electronics and electrical engineering and the Ph.D. degree from the University of Glasgow, Glasgow, U.K., in 1994 and 1997, respectively. He is a Full Professor with the Department of Computer Science, City University of Hong Kong, Hong Kong SAR. He has published over 200 refereed articles and six books. Dr. Tan is the Editor-in-Chief of the IEEE Transactions on Evolutionary Computation, was the Editor-in-Chief of the IEEE Computational Intelligence Magazine from 2010 to 2013, and currently serves as the Editorial Board Member of over 10 journals. He is currently an elected member of IEEE CIS AdCom and a Changjiang Chair Professor in China.