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讲准字261号:超多目标进化优化研究最新进展

发布时间:2019-09-25|浏览次数:

讲座报告主题:超多目标进化优化研究最新进展
专家姓名:Gary G. Yen
日期:2019-11-02 时间:10:00
地点:计算机学院208报告厅
主办单位:计算机科学与通信工程学院  

主讲简介:Gary G. Yen received the Ph.D. degree in electrical and computer engineering from the University of Notre Dame in 1992.  He is currently a Regents Professor in the School of Electrical and Computer Engineering, Oklahoma State University.  His research interest includes intelligent control, computational intelligence, evolutionary multiobjective optimization, conditional health monitoring, signal processing and their industrial/defense applications. Gary was an associate editor of the IEEE Transactions on Neural Networks and IEEE Control Systems Magazine during 1994-1999, and of the IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man and Cybernetics (Parts A and B) and IFAC Journal on Automatica and Mechatronics during 2000-2010.  He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics and IEEE Transactions on Emerging Topics on Computational Intelligence.  Gary served as Vice President for the Technical Activities, IEEE Computational Intelligence Society in 2004-2005 and was the founding editor-in-chief of the IEEE Computational Intelligence Magazine, 2006-2009.  He was the President of the IEEE Computational Intelligence Society in 2010-2011 and is elected as a Distinguished Lecturer for the term 2012-2014 and 2016-2018.  He received Regents Distinguished Research Award from OSU in 2009, 2011 Andrew P Sage Best Transactions Paper award from IEEE Systems, Man and Cybernetics Society, 2013 Meritorious Service award from IEEE Computational Intelligence Society and 2014 Lockheed Martin Aeronautics Excellence Teaching award. He is a Fellow of IEEE and IET.


主讲内容:进化计算是研究受自然进化与自适应规则启发的计算智能的一类分支,近年来,采用这种基于群体计算的启发式范式求解多目标优化问题受到越来越多的关注。对于复杂的多目标优化问题,甚至是包含不确定因素或随时间改变的动态目标或约束函数,多目标进化优化算法都能够进行有效地求解,并得到这些问题的Pareto最优解。然而,在处理超多目标优化问题时,由于Pareto最优规则导致进化算法选择压力过小,现有的多目标优化算法往往表现不佳。因此,为应对维数灾难所带来的挑战,进化算法的研究仍在持续发展。报告介绍并分析在实际应用中人们所关注的三个主要问题及解决方案,并着重介绍如何利用超多目标进化优化方法解决动态优化、约束优化、鲁棒优化的最新进展以及它们在包括深度神经网络自动设计上的应用。


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