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Recent Research Trends in Evolutionary Optimization: A Brief Glance
时间:2019-04-16 17:46    点击:   所属单位:电子工程学院
讲座名称 Recent Research Trends in Evolutionary Optimization: A Brief Glance
讲座时间 2019-04-22 11:15:00
讲座地点 西安电子科技大学北校区主楼三区201
讲座人 秦凯
讲座人介绍
Kai Qin is an Associate Professor at Swinburne University of Technology, Melbourne, Australia, currently serving as the Program Leader on “Data Analytics” in Swinburne Data Science Research Institute (DSRI), the Leader of DSRI’s Machine Learning and Intelligent Optimization Research Group, the Director of Swinburne’s Intelligent Data Analytics Lab, and the Course Director of Swinburne’s Master of Data Science Program. Prior to joining Swinburne, he worked at the Nanyang Technological University (Singapore), the University of Waterloo (Canada), INRIA (France) and RMIT University (Australia). His major research interests include evolutionary computation, machine learning,  intelligent data analytics (incl. image, text and time-series data), computer vision, remote sensing, GPU computing and services computing. He is currently co-chairing the IEEE Computational Intelligence Society (CIS) Task Forces on “Collaborative Learning and Optimization” and “Multitask Learning and Multitask Optimization”, and also serving as the Vice-Chair of the IEEE CIS Technical Committee on Neural Networks.
讲座内容
Evolutionary optimization studies a family of population-based metaheuristic optimization techniques inspired by nature, which has pervasively succeeded in solving versatille challenging optimization problems that are off limits to mathematical programming approaches. This talk will briefly introduce to several recent research trends with case studies in evolutionary optimization, including data-driven evolutionary optimization, evolutionary multi-task optimization, scalable parallel evolutionary optimization and evolutionary optimization for deep learning.
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