20150709-21 周爱民：Learning Guided Multiobjective Optimization

2015-7-4 13:19| 发布者: 彭玺ASTAR| 查看: 7431| 评论: 0

摘要: 报告嘉宾2：周爱民(华东师范大学)主持人：报告时间：2015年7月9日周四晚21:00（北京时间）报告题目：Learning Guided Multiobjective Optimization 文章信息：H. Zhang, A. Zhou, S. Song, Q. Zhang, X. Gao, and J. ...

【15-21期VALSE Webinar活动】

报告嘉宾2：周爱民 (华东师范大学)
主持人：蓝振忠（CMU）
报告时间：2015年7月9日周四晚21:00（北京时间）
报告题目：Learning Guided Multiobjective Optimization [Slides]
文章信息：
[1] H. Zhang, A. Zhou, S. Song, Q. Zhang, X. Gao, and J. Zhang, A self-organizing multiobjective evolutionary algorithm, 2015 (submit).
[2] A. Zhou, J. Sun, and Q. Zhang, An estimation of distribution algorithm with cheap and expensive local search, IEEE TEVC, 2015. (accepted)
[3] A. Zhou, and Q. Zhang, Are all the subproblems equally important? Resource allocation in decomposition based multiobjective evolutionary algorithms, IEEE TEVC, 2015. (accepted)
[4] W. Gong, A. Zhou, and Z. Cai, A multi-operator search strategy based on cheap surrogate models for evolutionary optimization, IEEE TEVC, 2015. (accepted)
[5] Z. Wang, Q. Zhang, A. Zhou, M. Gong, and L. Jiao, Adaptive replacement strategies for MOEA/D, IEEE TCYB, 2015. (accepted)
[6] A. Zhou, Y. Jin, and Q. Zhang, A population prediction strategy for evolutionary dynamic multiobjective optimization, IEEE TCYB, 44(1): 40-53,2014.
[7] A. Zhou, Q. Zhang, and Y. Jin, Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm, IEEE TEVC, 13(5): 1167-1189, 2009.
[8] Q. Zhang, A. Zhou, and Y. Jin, RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm, IEEE TEVC, 12(1): 797-799, 2008.
报告摘要：The population of an evolutionary algorithm can be regarded as a data set that contains some kind of patterns. Although some evolutionary algorithms, such as estimation of distribution algorithms which utilize the probability graphic models to extract the patterns and surrogate assisted evolutionary algorithms which use regression methods, try to find the patterns and to guide the evolution process, there is still lack of a systematic work on using statistical and machine learning techniques to guide the evolutionary optimization. Furthermore, the area of machine and statistical learning contains a large broad of techniques but only a few ones have been utilized in the community of evolutionary computation. This talk tries to build a bridge from statistical and machine learning to evolutionary optimization especially evolutionary multiobjective optimization. The talk will cover the background on multi objective optimization, an example on how to use self-organizing maps to assist the search, a short survey on our recent work on this topic, and some conclusions and remarks for future work.
报告人简介：Aimin Zhou is currently an Associate Professor with the Shanghai Key Laboratory of Multidimensional Information Processing, and the Department of Computer Science and Technology, East China Normal University, Shanghai, China. He received the B.Sc. and M.Sc. degrees from Wuhan University, Wuhan, China, in 2001 and 2003, respectively, and the Ph.D. degree from University of Essex, Colchester, U.K., in 2009, all in computer science. His research interests include evolutionary computation and optimization, machine learning, image processing, and their applications. He has published over 40 peer-reviewed papers. He received the best paper award in IES 2014. He is an Associate Editor of the Swarm and Evolutionary Computation.

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20150709-21 周爱民：Learning Guided Multiobjective Optimization

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