进化大规模全局优化概述
Evolutionary Large-Scale Global Optimization: An Introduction
李晓东   Xiaodong Li
报告人照片   Xiaodong Li received his Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. He is a full professor at the School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia. His research interests include evolutionary computation, neural networks and machine learning. He serves as an Associate Editor of the IEEE TEVC, SI, and IJSIR. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a Vice-chair of IEEE CIS Task Force of Multi-Modal Optimization, and a former Chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS “IEEE Transactions on Evolutionary Computation Outstanding Paper Award”.
  In this talk, we provide an overview of recent advances in the field of evolutionary large-scale global optimization with an emphasis on the divide-and-conquer approaches (a.k.a. decomposition methods). In particular, we give an overview of different approaches including the non-decomposition based approaches such as memetic algorithms and sampling methods to deal with large-scale problems. This is followed by a more detailed treatment of implicit and explicit decomposition algorithms in large-scale optimization. Considering the popularity of decomposition methods in recent years, we provide a detailed technical explanation of the state-of-the-art decomposition algorithms including the differential grouping algorithm and its latest improved derivatives (such as global DG and DG2 algorithms), which outperform other decomposition algorithms on the latest large-scale global optimization benchmarks. We also address the issue of resource allocation in cooperative coevolution and provide a detailed explanation of some recent algorithms such as the contribution-based cooperative co-evolution family of algorithms.
报告时间:2017年11月08日10时00分    报告地点:科大西区电二楼208会议室
报名截止日期:2017年11月08日    可选人数:40