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Seminar abstract

Surrogate-Assisted Evolutionary Optimisation - A Platform for Marrying Evolutionary Computation and Machine Learning

Yaochu Jin
University of Surrey, UK

Abstract: Evolutionary algorithms (EAs) have proven to be powerful for optimising problems that are non-differentiable, multi-modal, noisy and multi-disciplinary. However, EAs often require a large number of fitness evaluations to achieve acceptable solutions, which is prohibitive for computationally expensive problems. To address this issue, surrogate-assisted EAs, where computationally efficient models are used for fitness estimation to reduce computational cost, have attracted increasing attention in recent years. This talk presents a few examples illustrating how surrogate-assisted EAs can benefit from advanced machine learning techniques, including heterogeneous ensembles, semi-supervised learning and active learning. We finally suggest a few new avenues to explore to improve the computational efficiency and search performance of surrogate-assisted EAs.

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 currently a Professor and Chair in Computational Intelligence, Department of Computing, University of Surrey, UK, where he heads the Nature Inspired Computing and Engineering (NICE) Group. Before joining Surrey, he was a Principal Scientist and Group Leader with the Honda Research Institute Europe in Germany. His research interests include computational approaches to a systems-level understanding evolution, learning and development in biology, and bio-inspired methods for solving complex engineering problems. He won the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. His current research is funded by EU FP7, UK EPSRC and industries. Dr. Jin is an Associate Editor of BioSystems, the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on NanoBioscience, IEEE Computational Intelligence Magazine, and International Journal of Fuzzy Systems. He is also an Area Editor of Soft Computing. He is presently Chair of the Intelligent Systems Applications Technical Committee and an elected member of AdCom (2012-2014) of the IEEE Computational Intelligence Society. Dr. Jin has delivered over ten invited keynote speeches on morphogenetic robotics, developmental neural systems, modeling, analysis and synthesis of gene regulatory networks, evolutionary aerodynamic design optimization and multi-objective learning at international conferences. He is a Fellow of British Computer Society and Senior Member of IEEE. See for more details.
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