Abstract：This talk discusses the main challenges in data-driven surrogate-assisted evolutionary optimization of complex systems. Fundamental 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. Finally, a few examples of real-world data-driven optimization will be given.

Biography：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 funded by Finnish Funding Agency for Innovation and a Changjiang Scholar appointed by Ministry of Education, China. 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, 2013-2015) and was the Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015). He is the recipient of the 2018 “IEEE Transactions on Evolutionary Computation Outstanding Paper Award”, the 2014 and 2016 “IEEE Computational Intelligence Magazine Outstanding Paper Award”, the 2010 IEEE CIBCB “Best Paper Award”, the 2007 IEEE FOCI, and the 2017 IEEE CEC “Best Student Paper Award”. He is a Fellow of IEEE.