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特邀英国伯明翰大学姚新教授来校作学术报告
作者:科学技术处               发布时间:2015-8-26 10:51:59              浏览量:110


报告题目:Applications of Evolutionary Computation


报告人:姚新 教授 (http://www.cs.bham.ac.uk/~xin/)

CERCIA, School of Computer Science, University of Birmingham, UK


主持人:刘青山 院长


报告时间:2015年8月29日下午2:30


报告地点:图书馆七楼报告厅


欢迎广大师生踊跃参加!


江苏省大数据分析技术重点实验室


江苏省气象能源利用与控制工程技术研究中心


江苏省大气环境与装备技术协同创新中心


信息与控制学院


2015年8月26日



报告摘要:Evolutionary computation has enjoyed an incredible growth in recent years. This talk will highlight a few recent examples in evolutionary computation in terms of its applications, including data-driven modelling using the evolutionary approach in materials engineering, dynamic route optimisation for salting trucks, multi-objective design of hardware and software, neural network ensemble learning for pattern classification, and online ensemble learning in the presence of concept drifts. The primary objective of this talk is to illustrate novel applications of various evolutionary computation techniques, rather than to go into depth on any of the examples. It is aimed at people who are not specialists in evolutionary computation. However, I would be delighted to go into the depth on any of the topics if there is an interest.


报告人简介:Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (Centre of Excellence for Research in Computational Intelligence and Applications) at the University of Birmingham, UK. He is an IEEE Fellow and the President (2014-15) of IEEE Computational Intelligence Society (CIS). He won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards. He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. His major research interests include evolutionary computation, ensemble learning, and real-world applications.