中国计算机学会青年计算机科技论坛

CCF Young Computer Scientists & Engineers Forum – Guilin Branch

CCF   YOCSEF桂林

“进化计算技术与应用”学术报告会

于2018年6月12日（星期二）上午9:00-11:00

在桂林电子科技大学金鸡岭校区10教516举行

敬请光临！

执行主席：常亮，何倩

讲座题目：State-Of-The-Art Evolutionary Algorithms for Many Objective Optimization

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 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.

报告人简介：Gary G.Yen（加里 烟淦）教授于 1992 年在美国圣母大学获电气与计算机工程专业的博士学位。目前为美国俄克拉荷马州立大学电气与计算机工程学院讲席教授(Regents Professor)，IEEE 和 IET Fellow。Yen 教授是国际智能控制、计算智能、进化多目标优化等领域著名专家学者，在智能控制、多目标优化、健康监测及其工业/国防应用做出了突出的工作。2010-2011 年，任 IEEE 计算智能协会主席。先后主持了 2006 年和 2016 年的 IEEE 计算智能世界大会。曾担任 IEEE Transactions on Neural Networks, IEEE Control Systems Magazine, IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems Man and Cybernetics 的副主编。目前是 IEEE Transactions on Evolutionary Computation, IEEE Transactions on Emerging Topics on Computational Intelligence 和 IEEE Transactions on Cybernetics 的副主编。此外，Gary G.Yen 教授还是 IEEE Computational Intelligence Magazine 的创始主编。2009 年获得了 OSU 颁发的杰出研究奖。2011 年获得 IEEE 系统、人与控制协会的 Andrew P Sage 最佳会刊论文奖。2013 年获得由 IEEE 计算智能协会颁发的卓越功勋奖。2014 年获得 Lockheed Martin 航空卓越教学奖。