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Towards 'General Optimization Intelligence'
时间:2019-04-16 13:40    点击:   所属单位:电子工程学院
讲座名称 Towards 'General Optimization Intelligence'
讲座时间 2019-04-22 10:30:00
讲座地点 西安电子科技大学北校区主楼三区201
讲座人 Yew-Soon Ong
讲座人介绍
Yew-Soon Ong教授目前就职于新加坡南洋理工大学计算机科学与工程学院,担任科学与人工智能研究中心(DSAIR)的创始主任,A * Star SIMTech-NTU 复杂系统联合实验室(SCALE@NTU)的联合主任。他长期致力于人工与计算智能、Memetic计算、演化与迁移优化、机器学习等方面的研究。他在计算智能领域取得了多项创新成果,已发表了超过 180 篇论文。 Google Scholar 引用为 10350,H-index 为 45。他曾三次荣获国际一流期刊的杰出论文奖,连续两年荣膺汤森路透“高被引学者”。因在人工智能与计算智能及应用方面的突出贡献,于 2017 年被授予 IEEE Fellow。同时,他还是多个国际一流期刊和 IEEE Trans. on Emerging Topics of Computational Intelligence)的主编或副主编。目前,其担任 IEEE Trans. on Computational Intelligence and AI in Games 指导委员会的秘书长。
讲座内容
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 provided, with illustrative examples of adaptive knowledge transfer across problems from diverse areas, including, operations research, engineering design, and neuro-evolution.
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