报告题目：Evolutionary Multiobjective Optimization Made Faster

报告地点：金花校区教6楼1115会议室

报告时间：2021年4月28日 晚  19：15

报告人简介：

周爱民研究员，博士生导师，华东师范大学上海智能教育研究院副院长、计算机科学与技术学院副院长（主持工作）。目前担任Swarm and Evolutionary Computation、Complex & Intelligent Systems等期刊副主编或编委。主要研究方向为演化搜索与优化、机器学习和智能教育。相关研究成果发表于IEEE TEVC、IEEE TCYB、IEEE TNNLS、AAAI、软件学报、计算机学报等期刊和会议，这些成果SCI他引2300余次，Google Scholar引用5500余次。相关研究成果在金融交易、工业优化设计、智慧教育等领域获得应用。

 

报告摘要：In 1880s, economics Professors F. Y. Edgeworth and V. Pareto started to study the optimality of multiobjective optimization problems (MOPs), which created a new field of research area. Unlike traditional optimization problems, the optimality of an MOP usually consists of a set of tradeoff solutions, which are called Pareto optimal solutions. In later of 1990s, it is possible for the first time to approximate the whole Pareto optimal solutions of an MOP in a single run by using evolutionary algorithms, after more than a century of research. Our work aims to make evolutionary multiobjective optimization faster by using problem specific knowledge either given by the decision makers or extracted online by machine learning techniques. This talk will briefly outline the recent advances in evolutionary multiobjective optimization, introduce our work on making evolutionary multiobjective faster, and present some examples in artificial intelligence and engineering areas.