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State-Of-The-Art Evolutionary Algorithms for Many Objective Optimization
时间:2018-03-22 17:45    点击:   所属单位:人工智能学院
讲座名称 State-Of-The-Art Evolutionary Algorithms for Many Objective Optimization
讲座时间 2018-03-24 10:00:00
讲座地点 主楼II-402
讲座人 Gary G. Yen
讲座人介绍 Biography:

Gary G. Yen received the 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 Emerging Topics on Computational Intellifgence and IEEE Transactions on Cybernetics.  Gary served as Vice President for the Technical Activities, IEEE Computational Intelligence Society in 2004-2005 and is 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 is elected as a Distinguished Lecturer for the terms 2012-2014 and again 2016-2018.  He chaired 2006 IEEE World Congress on Computational Intelligence and again 2016 IEEE World Congress on Computational Intelligence, both held in Vancouver, Canada.  He received Regents Distinguished Research Award from OSU 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 serves as the chair of IEEE/CIS Fellow Committee.  He is a Fellow of IEEE and IET.

讲座内容 Abostract:

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 in solving multi-objective optimization problems have been receiving a growing attention.  To search for a family of Pareto optimal solutions based on nature-inspiring metaphors, Evolutionary Multi-objective Optimization Algorithms have been successfully exploited to solve optimization problems in which the fitness measures and even constraints are uncertain and changed over time.

When encounter optimization problems with many objectives, nearly all current designs perform poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto optimality principle.  In addition to various Many-Objective Evolutionary Algorithms proposed in the last few years, this talk will be devoted to address three issues to complete the real-world applications at hand- visualization, performance metrics and multi-criteria decision-making for the many-objective optimization.  Visualization of population in a high-dimensional1 objective space throughout the evolution process presents an attractive feature that could be well exploited in designing many-objective evolutionary algorithms.  A performance metric tailored specifically for many-objective optimization is also designed, preventing various artifacts of existing performance metrics violating Pareto optimality principle.  A minimum Manhattan distance (MMD) approach to multiple criteria decision making in many-objective optimization problems is detailed.  The approach selects the final solution corresponding with a vector that has the MMD from a normalized ideal vector.  This procedure is equivalent to the knee selection described by a divide and conquer approach that involves iterations of pairwise comparisons.  Being able to systematically assign weighting coefficients to multiple criteria, the MMD approach is equivalent to a weighted-sum approach. Because of the equivalence, the MMD approach possesses rich geometric interpretations that are considered essential in the field of evolutionary computation.

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