学术讲座【Recent work on intelligent computation】

浏览次数: 13 发布时间: 2017-04-28

时间:201752日(星期二)上午930

 

地点:旗山校区软件学院512

 

主讲:南方科技大学(英国伯明翰大学)姚新教授

 

主办:软件学院

 

专家简介:Xin Yao is a part (Professor) of Computer Science and the Director of  CERCIA(Centre of Excellence for Research in Computational Intelligence  andApplications) at the University of Birmingham, UK. He is a chair  professorship at the Department of Computer Science and Engineering, Southern  University of Science and Technology (SUSTech), Shenzhen, China, in Fall 2016.  He is an IEEE Fellow andthe President (2014-15) of IEEE Computational  Intelligence Society (CIS).He won the 2001 IEEE Donald G. Fink Prize Paper  Award, 2010 IEEE Transactionson Evolutionary Computation Outstanding Paper  Award, 2010 BT Gordon RadleyAward for Best Author of Innovation (Finalist), 2011  IEEE Transactions onNeural Networks Outstanding Paper Award, and many other best  paper awards.He won the prestigious Royal Society Wolfson Research Merit Awardin  2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013.His major  research interests include evolutionary computation, ensemblelearning, and  real-world applications.

报告摘要:Computational intelligence has been used in software engineering for  a long time. There has been a recent surge in interest in this area, especially  in search-based software engineering. This talk touches upon some of the recent  examples in the broader field of computational intelligence in software  engineering. It is highlighted that software engineering could benefit  fromadvanced computational intelligence techniques in tackling hard  problems,e.g., software module clustering, software reliability maximisation,  software project scheduling, software effort estimation, software defect  prediction, etc. It is also argued that new research challenges posed by  software engineering could stimulate further development of new theories and  algorithms in computational intelligence. Such theoretical research could shed  some light on important research issues and provide guidance in future work. For  example, theoretical analysis of computational time complexity of search  algorithms can inform us about the limitation of search-based software  engineering. The research in online learning algorithms can help us develop  novel approaches to software effort estimation when historical data within a  company are sparse. The primary aim of this talk is not to provide a  comprehensive review of computational intelligence for software engineering, but  to illustrate the opportunities for further research and development in this  area through selected examples.