IEEE Fellow、南方科技大学讲席教授Hisao Ishibuchi学术报告

作者: 时间:2023-09-22 点击数:

应郑州大学电气与信息工程学院邀请,IEEE Fellow、南方科技大学讲席教授Hisao Ishibuchi为我院师生做学术报告。

报告题目:New Research Directions in Evolutionary Multi-Objective Optimization

报告人:Hisao Ishibuchi讲席教授



报告摘要:In the field of evolutionary multi-objective optimization (EMO), various EMO algorithms have been proposed in the last three decades under the following widely-accepted implicit assumption: The final population is presented to the decision maker. Thus, the goal of EMO algorithm design is to find a good final population, and the performance of EMO algorithms is evaluated using the final population. Recently, two different directions have been actively studied. One is the use of an unbounded external archive, and the other is the Pareto front (or Pareto set) modelling. In the first direction, all the examined solutions are stored in the archive. Then, an arbitrary number of solutions can be selected from the archive for the decision maker. In the second direction, the Pareto front (or Pareto set) is modelled by a machine learning technique using examined solutions. Then, an arbitrary number of final solutions can be obtained from the model for the decision maker. These two approaches are much more flexible than the traditional EMO algorithm framework since an arbitrary number of solutions can be presented to the decision maker, which may increase the practical usefulness of EMO-based decision making. In this talk, first I will explain the traditional EMO framework and the two new directions (the use of unbounded external archive, and the modelling of the Pareto front/set). Then, I will discuss some interesting research challenges related to the two new directions such as subset selection from large candidate sets for multi-modal multi-objective optimization, use of examined solutions for generating new solutions, and the inverse mapping from the m-dimensional objective space to the n-dimensional decision space where n >> m.

报告人简介:Hisao Ishibuchi教授, IEEE Fellow,  IEEE CIS杰出讲师,现为南方科技大学计算机系讲座教授。主要研究领域为计算智能,包括进化计算、模糊系统、神经网络及其混合系统等。Hisao Ishibuchi教授在2010-2013年担任IEEE计算智能学会副主席, 现为IEEE CIS Administrative Committee Member2014-2019年担任该领域顶级学术期刊IEEE Computational Intelligence Magazine的主编,并担任IEEE Transactions on Evolutionary ComputationIEEE Transactions on Cybernetics IEEE Access等计算智能领域权威期刊的副编。现为2024IEEE WCCI的大会主席。Hisao Ishibuchi教授的谷歌学术总引次数超过34000H-index 84Hisao Ishibuchi教授的研究成果曾获得多次奖励,包括2019IEEE CIS Fuzzy Systems Pioneer Award2020IEEE Trans. on Evolutionary Computation杰出论文奖、FUZZ-IEEE 2009FUZZ-IEEE 2011EMO 2019GECCO 2004GECCO 2017GECCO 2018GECCO 2020GECCO 2021最佳论文奖、2023IEEE CIS Enrique Ruspini Award for Meritorious Service等。

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