Towards 'General Optimization Intelligence'
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 | ![]() |
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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如果您有学术信息或学术动态,欢迎投稿。我们将在第一时间确认并收录,投稿邮箱: meeting@xidian.edu.cn