Title: Many-Objective Evolutionary Algorithms for Optimization
Abstract: Evolutionary computation is the study of biologically motivated computational paradigms which exert novel ideas and inspiration from natural evolution and adaptation. The applications of population-based heuristics and nature-inspired metaphors in solving multiobjective optimization problems have been receiving a growing attention. To search for a family of Pareto optimal solutions, Evolutionary Multiobjective Optimization Algorithms have been successfully exploited to solve optimization problems in which the fitness measures and even constraints could be uncertain and varied over time. When encounter optimization problems with many objectives, nearly all designs performs poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto optimality principle. This tutorial will survey recently published literature along this line of research- evolutionary algorithm for many-objective optimization and its real-world applications. Specifically, selection strategy, including mating selection and environmental selection, is a key ingredient in the design of evolutionary many-objective optimization algorithms. We will provide a comprehensive analysis on the selection strategies in the current evolutionary many-objective optimization algorithms. Experimental results on scalable DTLZ and WFG benchmark functions will demonstrate the pros and cons of various designs in terms of chosen performance metrics designed specifically for many-objective optimization. Based on performance metrics ensemble, we will provide a comprehensive measure among all competitors and more importantly reveal insight pertaining to specific problem characteristics that each evolutionary many-objective optimization algorithm could perform the best. The experimental results confirm the finding from the No Free Lunch theorem that any algorithm’s elevated performance over one class of problems is exactly paid for in loss over another class.
Gary G. Yen received his Ph.D. degree in electrical and computer engineering from the University of Notre Dame in 1992. He is currently a Regents Professor in the School of Electrical and Computer Engineering, Oklahoma State University. His research interest includes intelligent control, computational intelligence, evolutionary multiobjective optimization, conditional health monitoring, signal processing and their industrial/defense applications. Gary was an associate editor of the IEEE Transactions on Neural Networks and IEEE Control Systems Magazine during 1994-1999, and of the IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man and Cybernetics and IFAC Journal on Automatica and Mechatronics during 2000-2010. He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics and International Journal of Swarm Intelligence Research. Gary served as Vice President for the Technical Activities, IEEE Computational Intelligence Society in 2004-2005 and was the founding editor-in-chief of the IEEE Computational Intelligence Magazine, 2006-2009. He was the President of the IEEE Computational Intelligence Society in 2010-2011 and was elected as a Distinguished Lecturer for the term 2012-2014. He received Regents Distinguished Research Award from the University in 2009, 2011 Andrew P Sage Best Transactions Paper award from IEEE Systems, Man and Cybernetics Society, 2013 Meritorious Service award from IEEE Computational Intelligence Society, and 2014 Lockheed Martin Aeronautics Excellence Teaching award. Currently he chair the Awards Committee of the IEEE Computational Intelligence Society and is a member of IEEE Fellow Committee, 2012-2015. He is a Fellow of IEEE and IET.