Colloquia / Seminars / Industry Talks

Many-objective optimization problems (ManyOPs) pose challenges to existing multi-objective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and computational complexity. This talk presents a personal view towards various strategies and methods for coping with many objectives, from simple ideas of more efficient non-dominated sorting and nonlinear dimensionality reduction, to other simple ideas of a two-archive algorithm (i.e., Two_Arch2), which uses two separate archives to focus on convergence and diversity, respectively. Different selection principles (indicator-based and Pareto-based) are used in the two archives. A new Lp-norm based diversity maintenance scheme is introduced. Our experimental results show that Two_Arch2 can cope with ManyOPs (up to 20 objectives) with satisfactory convergence, diversity, and complexity.

Xin Yao is a Chair Professor of Computer Science in the Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China. He is an IEEE Fellow and a Distinguished Lecturer of IEEE Computational Intelligence Society (CIS). He previously served as the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation and the President (2014-15) of IEEE CIS. His main research interests include evolutionary computation, ensemble learning, and their applications, especially in software engineering. His papers won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 and 2015 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards. He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013.