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金耀初教授学术报告会

2017-08-24

报告题目: Surrogate-assisted evolutionary optimization of expensive optimization problems

报告摘要:This talk discusses fundamental ideas and methodologies for surrogate-assisted evolutionary optimization. Basic issues such as surrogate model selection, surrogate model management and model training using advanced machine learning techniques in single and multi- and many-objective optimization will be discussed. Challenges and recent advances in surrogate-assisted optimization of high-dimensional expensive optimization problems will be presented.

报 告 人:金耀初教授(英国University of Surrey计算机系首席教授)

时  间:  2017年8月29日下午 14:30

地  点:  磬苑校区理工D楼318室

欢迎各位老师、同学届时前往!

主办单位:计算机科学与技术学院


报告人简介:

Yaochu Jin received the B.Sc., M.Sc., and  Ph.D. degrees from Zhejiang  University,Hangzhou, China, in 1988, 1991, and 1996 respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001.

He is a Professor in Computational Intelligence, Department of Computer Science,University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He is also a Finland Distinguished Professor and a Changjiang Scholar. His main research interests include evolutionary computation, machine learning, computational neuroscience, and evolutionary developmental systems.  He has published over 200 journal and conference papers and has been granted eight US/EU/Japan patents.


Dr Jin is the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, and Co-Editor-in-Chief of Complex & Intelligent Systems.He is an IEEE Distinguished Lecturer (2017-2019) and was the Vice President for Technical Activities of the IEEE Computational Intelligence Society(2014-2015). He is the recipient of the 2017 “IEEE Transactions on Evolutionary Computation Outstanding Paper Award”, the 2014 and 2016 “IEEE Computational Intelligence Magazine Outstanding Paper Award”, the 2010 IEEE CIBCB “Best PaperAward”, the 2007 IEEE FOCI, and the 2017 IEEE CEC “Best Student Paper Award”.He is a Fellow of IEEE.