HONG KONG BAPTIST UNIVERSITY
FACULTY OF SCIENCE

Department of Computer Science Colloquium
2017 Series

A Self-Organizing Multiobjective Evolutionary Algorithm

Prof. Aimin Zhou
Professor
Department of Computer Science and Technology
East China Normal University

Date: April 6, 2017 (Thursday)
Time: 3:30 - 4:30 pm
Venue: WLB207, Lam Woo International Conference Centre, Shaw Campus

Abstract
Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m−1)-dimensional piecewise continuous manifold. Based on this property, this work proposes a self-organizing multiobjective evolutionary algorithm. At each generation, a self-organizing mapping method with (m − 1) latent variables is applied to establish the neighborhood relationship among current solutions. A solution is only allowed to mate with its neighboring solutions to generate a new solution. To reduce the computational overhead, the self-organizing training step and the evolution step are conducted in an alternative manner. In other words, the self-organizing training is performed only one single step at each generation. The proposed algorithm has been applied to a number of test instances and compared with some state-of-the-art multiobjective evolutionary methods. The results have demonstrated its advantages over other approaches.

Biography
Dr. Aimin Zhou is currently a Professor with the Department of Computer Science and Technology, East China Normal University, Shanghai, China. He received the B.Sc. and M.Sc. degrees from Wuhan University, Wuhan, China, in 2001 and 2003, respectively, and the Ph.D. degree from University of Essex, Colchester, U.K., in 2009, all in computer science. His research interests include evolutionary computation and optimization, machine learning, image processing, and their applications. He has published over 50 peer-reviewed papers, and received the best paper award in IES 2014. He is an Associate Editor of the Swarm and Evolutionary Computation, the Complex & Intelligent Systems, and the Swarm Intelligence and Numerical Methods.

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