讲座题目：Towards General Optimization Intelligence 主讲人：Yew-soon Ong 主持人👱♂️😡：周爱民 副院长 开始时间：2019-12-18 13:00:00 讲座地址👮♀️：中北校区理科大楼B504 主办单位：计算机科学与技术学院
报告人简介： Yew-soon, Ong received a PhD degree for his work on Artificial Intelligence in complex design from the University of Southampton, United Kingdom in 2003. He is currently a President’s Chair Professor of Computer Science, Professor (Cross Appointment) with School of Physical and Mathematical Science at the Nanyang Technological University, Singapore, and holds the position of Chief Artificial Intelligence Scientist at the Agency for Science, Technology and Research of Singapore. Concurrently he is Director of the Data Science and Artificial Intelligence Research Center, co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab and co-Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems. He was Chair of the School of Computer Science and Engineering, Nanyang Technological University from 2016-2018, Lead of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab from 2013-2016 and Director of the Centre for Computational Intelligence from 2008-2015. His research interest lies in artificial & computational Intelligence, mainly optimization intelligence and machine learning. He is a Fellow of the IEEE and Editor-In-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence. He was listed among the World's Most Influential Scientific Minds and a Thomson Reuters Highly Cited Researcher. Several of his research publications have received IEEE outstanding paper awards. 报告内容： Traditional Optimization tends to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of optimization solvers do not automatically grow with experience. In contrast however, humans routinely make use of a pool of knowledge drawn from past experiences whenever faced with a new task. This is often an effective approach in practice as real-world problems seldom exist in isolation. Similarly, practically useful artificial systems are expected to face a large number of problems in their lifetime, many of which will either be repetitive or share domain-specific similarities. This view naturally motivates advanced optimizers that can replicate human cognitive capabilities, leveraging on lessons learned from the past to accelerate the search towards optimal solutions of never before seen tasks. With the above in mind, this talk aims to shed light on recent research advances in the field of global black-box optimization that champion the general theme of ‘General Optimization Intelligence’. A brief overview of associated algorithmic developments in memetic computation and Bayesian optimization shall be considered, with illustrative examples of adaptive knowledge transfer across problems from diverse areas, including, operations research, engineering design, and machine learning problems. |
