报告题目：Learning Guided Evolutionary Multiobjective Optimization
报告人： 周爱民 研究员 华东师范大学
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邀请人：高卫峰 副教授
报告时间：2018-09-08  9:00
报告地点：信远楼II206williamhill威廉希尔官网报告厅
报告人简介：
周爱民，博士，研究员，博士生导师。主要研究方向为演化计算与最优化、机器学习、图像处理和应用。分别于2001年和2003年在武汉大学获得计算机学士和硕士学位，2009年在英国Essex大学获得计算机博士学位，2009年起在华东师范大学工作。在IEEE TEVC、IEEE TCYB等权威期刊和会议发表70余篇学术论文，获IES 2014最优论文，Google Scholar引用量3300余次。担任Swarm and Evolutionary Computation、Complex & Intelligent Systems等期刊副编或编委，参与创办演化计算与优化(ECOLE)研讨会并担任2016年会议主席。
报告摘要：Learning guided evolutionary optimization utilizes statistical & machine learning techniques to assist the evolutionary algorithms. The learning techniques can be used to extract the problem and algorithm information online and thus to improve the algorithm performance. When using learning techniques in evolutionary algorithms, there arises a variety questions, such as why using learning techniques, which learning techniques to use, and how to use learning techniques. In this talk, we firstly try to answer these questions by some analysis. Then from the angle of algorithm design, i.e., initialization, reproduction, selection, stop condition, and algorithm tuning, we give some examples to show how to applying learning techniques to evolutionary multiobjective optimization.