中文版：
题目：Kay Chen Tan 教授学术报告通知：复杂多目标优化的可学习进化算法
报 告 人：Kay Chen Tan 教授
工作单位：香港理工大学
报告题目：复杂多目标优化的可学习进化算法
报告时间：2022年9月1日（周四）16:00-17:00
会议网址：https://meeting.tencent.com/dm/GycDo4JgEHPP （腾讯会议）
（会议号：264 688 331）
内容摘要：
以基于种群迭代搜索方法为特征的进化算法是解决不同场景多目标优化问题 (MOP) 的有效工具。针对复杂问题，可行解数量随目标函数的增加呈指数增长，搜索空间随设计变量的递增亦呈指数扩张，因此，在目标空间学习自定义有效环境选择策略，并在简化空间中增强搜索能力，对于提升模型可扩展性至关重要。此外，由于优化问题很少孤立存在，解决一个问题（或任务）的先验知识可能有助于其他相关问题的优化。这与人类的学习行为一致，即能够利用过去有用的经验解决手头相关的问题，并在面临多个看似无关问题时挖掘潜在的协同作用。本报告将重点介绍我们最近关于可扩展和可学习多目标优化的工作，以解决各种类型的复杂问题，如多目标优化问题、大规模多目标以及多任务多目标问题，并就未来的研究方向进行一些讨论。
个人简介：
Kay Chen Tan（IEEE Fellow）教授目前担任香港理工大学计算机系副主任、计算智能首席教授，IEEE 计算智能学会副主席及斯普林格系列著作《Machine Learning: Foundations, Methodologies, and Applications》联合主编，IEEE Distinguished Lecturer及英国诺丁汉大学名誉教授。Kay Chen Tan 教授于2010-2013 年曾任《IEEE Computational Intelligence Magazine》主编，2015-2020年任《IEEE Transactions on Evolutionary Computation》主编。目前，陈家进教授已出版7本专著，发表了230余篇高质量期刊论文。
编辑：王健
英文版
题目：Academic Report Notice of Kay Chen Tan：Learnable Evolutionary Algorithms for Complex Multiobjective Optimization
内容：Speaker: Professor Kay Chen Tan
Title: Learnable Evolutionary Algorithms for Complex Multiobjective Optimization
Time: 16:00-17:00, September 1, 2022 (Thursday)
Website: https://meeting.tencent.com/dm/GycDo4JgEHPP（Tencent meeting）
(meeting number:264 688 331）
Abstract:
Evolutionary algorithms characterized by a population-based iterative search approach have been recognized as effective tools for addressing multiobjective optimization problems (MOPs) in different scenarios. Since the number of solutions grows exponentially with the number of objective functions and the search space expands exponentially with the number of design variables, learning to customize efficient environmental selection strategies in the objective space as well as simplifying the search space and enhancing the search capability in the variable space are crucial for good scalability in solving complex problems. Besides, as optimization problems seldom exist in isolation, the experience of solving one problem (or task) may learn useful knowledge to assist the optimization of other related ones. This is consistent with the learning behavior of human beings that useful knowledge from past experiences can be exploited to solve relevant problems at hand and potential synergies may be excavated when facing multiple seemingly unrelated problems. This talk will highlight some of our recent works on scalable and learnable multiobjective optimization to tackle various types of complex problems, i.e., many-objective optimization problems, large-scale MOPs, and multitasking MOPs. Some discussions on future research directions will also be given.
Personal Introduction:
Kay Chen Tan is currently a Chair Professor (Computational Intelligence) and Associate Head (Research and Developments) of the Department of Computing, The Hong Kong Polytechnic University. He has co-authored 7 books and published over 230 peer-reviewed journal papers. Prof. Tan is currentlythe Vice-President (Publications) ofIEEE Computational Intelligence Society, USA. He was the Editor-in-Chief of IEEE Transactions on Evolutionary Computation from 2015-2020, and IEEE Computational Intelligence Magazine from 2010-2013. Prof. Tan is an IEEE Fellow, an IEEE Distinguished Lecturer Program (DLP) speaker, and an Honorary Professor at the University of Nottingham in UK. He also serves as the Chief Co-Editor of Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications.
[Editor: Jian Wang]