报告人：Hisao Ishibuchi，南方科技大学讲席教授，IEEE Fellow

报告时间：2023年11月21日（星期二） 10:00 - 11:00

报告地点：信息科学与工程学院 624会议室

报告摘要：In the field of evolutionary multi-objective optimization (EMO), early EMO algorithms in the 1990s are called non-elitist algorithms where no solutions in the current population are included in the next population. That is, the next population is the offspring population of the current population. This non-elitist algorithm framework is clearly inefficient since we cannot preserve good solutions during the execution of EMO algorithms. As a result, almost all EMO algorithms in the last two decades are based on the elitist framework where the next population is selected from the current population and its offspring population. In both frameworks, the final population is presented to the decision maker as the final output from EMO algorithms. Recently, some potential difficulties of the elitist framework have been pointed out. One is that the final population is not always the best subset of all the examined solutions. It was demonstrated in the literature that some solutions in the final population are dominated by other solutions generated and deleted in previous generations. It is also difficult to utilize solutions in previous generations to generate new solutions. Offspring are always generated from solutions in the current population. Another difficult is that only a limited number of solutions (i.e., only solutions in the final population) are obtained. A new framework with an unbounded external archive can easily handle these difficulties since the final solution set is selected from all the examined solutions. In this framework, we can select an arbitrary number of solutions as the final output from EMO algorithms. Stored solutions in the external archive can be used to create new solutions and also to select solutions for the next population. In this talk, some interesting research issues in the new EMO algorithm framework are explained.

在进化多目标优化（EMO）领域，上世纪90年代早期的EMO算法被称为非精英算法，其中当前种群中没有解被包括在下一个种群中。换句话说，下一个种群是当前种群的后代种群。这种非精英算法框架显然效率低下，因为在EMO算法执行过程中无法保留良好的解决方案。因此，过去二十年几乎所有的EMO算法都基于精英框架，其中下一个种群是从当前种群及其后代种群中选择的。在这两种框架中，最终种群被呈现给决策者作为EMO算法的最终输出。最近指出了精英框架的一些潜在困难。其中一个是最终种群并不总是所有检验过的解的最佳子集。文献中证明了最终种群中的一些解被之前生成并在先前世代中删除的其他解支配。利用先前世代的解来生成新解也很困难。后代始终是从当前种群的解中生成的。另一个困难是仅获得有限数量的解（即仅最终种群中的解）。具有无界外部存档的新框架可以轻松处理这些困难，因为最终解集是从所有检验过的解中选择的。在这种框架中，我们可以从EMO算法中选择任意数量的解作为最终输出。外部存档中存储的解可用于生成新解，也可用于选择下一个种群的解。在本次演讲中，将解释新EMO算法框架中的一些有趣的研究问题。

报告人简介：Hisao Ishibuchi received the BS and MS degrees from Kyoto University in 1985 and 1987, respectively, and the PhD degree from Osaka Prefecture University in 1992. He is a Chair Professor at Southern University of Science and Technology, China. He was the IEEE Computational Intelligence Society (CIS) Vice-President for Technical Activities in 2010-2013 and the Editor-in-Chief of IEEE Computational Intelligence Magazine in 2014-2019. Currently he is an IEEE CIS Administrative Committee Member (2014-2019, 2021-2023), and an IEEE CIS Distinguished Lecturer (2015-2017, 2021-2023). He is also General Chair of IEEE WCCI 2024 in Yokohama, Japan. He received a Fuzzy Systems Pioneer Award from IEEE CIS in 2019, an Outstanding Paper Award from IEEE Trans. on Evolutionary Computation in 2020, an Enrique Ruspini Award for Meritorious Service from IEEE CIS in 2023, and Best Paper Awards from FUZZ-IEEE 2009, 2011, EMO 2019, and GECCO 2004, 2017, 2018, 2020, 2021. He also received a JSPS prize in 2007. He is an IEEE Fellow.

Hisao Ishibuchi分别于1985年和1987年在京都大学获得学士和硕士学位，并于1992年在大阪府立大学获得博士学位。他是中国南方科技大学的讲席教授。2010年至2013年担任IEEE计算智能学会（CIS）技术活动副主席，2014年至2019年担任IEEE计算智能杂志的主编。目前是IEEE CIS行政委员会成员（2014-2019年，2021-2023年），也是IEEE CIS杰出讲师（2015-2017年，2021-2023年）。此外，他是2024年IEEE WCCI在日本横滨的大会主席。2019年获得IEEE CIS颁发的模糊系统先驱奖，2020年获得IEEE进化计算期刊的优秀论文奖，2023年获得IEEE CIS颁发的恩里克·鲁斯皮尼杰出服务奖，并分别在FUZZ-IEEE 2009、2011年，EMO 2019年以及GECCO 2004、2017、2018、2020、2021年获得最佳论文奖。他还于2007年获得了日本学术振兴会的奖项。他是IEEE会士。

邀请人：刘益萍

联系人：刘益萍