报告题目：Population-based Search Methods for Multimodal Optimization Abstract
报 告 人：唐珂教授
时间：2017年 2月15号14:30-16:00
地点：安徽大学磬苑校区理工D楼318会议室
主办单位：计算机科学与技术学院
欢迎各位老师、同学届时前往！
科学技术处
2017年2月10日
报告人简介：
Ke Tang
Biography
Ke Tang is a Professor at the School of Computer Science and Technology, University of Science and Technology of China. His major research interests include evolutionary computation and machine learning and has authored/co-authored more than 100 refereed publications on prestigious journals and conferences, including IEEE TEVC, IEEE TNNLS, IEEE TCYB, IEEE TRO and IEEE TGRS, which received more than 4000 citations. He received the Royal Society Newton Advanced Fellowship in 2015 and is an Associate Editor or Editorial Board Member of the IEEE Trans. on Evolutionary Computation, Swarm and Evolutionary Computation (Elsevier), Natural Computing (Springer) and Memetic Computing (Springer).
报告内容：
The past two decades have witnessed the emerging needs of solving, particularly using computational approaches, hard optimization problems that affect our daily life. Most of these hard problems are multimodal ones and sometimes with non-differentiable objective functions. Population-based heuristic search methods are one of the leading approaches that can address such problems. This talk will first illustrate some multimodal optimization problems. Then, recent progresses on population-based search methods will be presented with successful applications.