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美国Oklahoma State University大学 Gary G Yen教授学术报告
作者:王添 发布日期:2018-10-12 9:13:00

报告题目:STATE-OF-THE-ART EVOLUTIONARY ALGORITHMS FOR MANY OBJECTIVE OPTIMIZATION

人:Gary G. Yen 教授  

报告时间:1026日(周五)下午14:30--16:00

报告地点:数理楼221

报告摘要: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 multiobjective optimization problems have been receiving a growing attention. To search for a family of Pareto optimal solutions based on nature-inspiring metaphors, Evolutionary Multiobjective 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 uation 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.

个人简历:Gary G. Yen, 计算智能领域国际著名专家,IEEE Fellow IET Fellow,在美国高水平大学担任讲席教授。近五年来,以第一作者和通讯作者身份在信息科学国际顶级期刊IEEE汇刊系列上发表文章20篇,其中12篇文章发表在影响因子10.629的期刊《IEEE Transactions on Evolutionary Computation》上;作为项目负责人主持4项科研项目,包含3项美国国家自然科学基金;担任IEEE汇刊系列下2SCI 期刊的副主编,期刊影响因子分别为10.6297.384。担任五项国际重要学术会议的主席职务,包括在2016 IEEE世界计算智能大会 (WCCI 2016) 担任大会主席;此大会是计算智能领域规模最大的学术与技术盛会,2016年共有来自世界各地的近2000名专家学者参会。候选人还担任在巴西举办的2018 IEEE进化计算大会 (CEC 2018)的大会主席。此外,候选人作为IEEE计算智能协会前任主席,2013年获得IEEE计算智能学会功勋奖,并于2012-20142016-2018年度被遴选为IEEE计算智能学会杰出讲师;20142015年当选IEEE智能协会颁奖委员会主席,2016-2017年担任IEEE协会院士委员会主席。

 
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