报告题目：Meta-Heuristic Combing Prior, Online and Offline Information for the Quadratic Assignment Problem
报告人：孙建永博士
主持人：周爱民
时间：2013年4月12日（星期五）下午2：00
地点：闵行校区信息楼629
报告摘要：The construction of promising solutions for NP-hard combinatorial optimisation problems (COPs) in meta-heuristics is usually based on three types of information, namely a priori information, a posteriori information learned from visited solutions during the search procedure, and online information collected in the solution construction process. Prior information reflects our domain knowledge about the COPs. Extensive domain knowledge can surely make the search effective, yet it is not always available. Posterior information could guide the meta-heuristics to globally explore promising search areas, but it lacks of local guidance capability. On the contrary, online information can capture local structures, its application can help exploit the search space. In this paper, we studied the effects of using these information on meta-heuristic's algorithmic performances for the COPs. The study was illustrated by a set of heuristic algorithms developed for the quadratic assignment problem (QAP). We first proposed an improved scheme to extract online local information, then developed a unified framework under which all types of information can be combined readily. Finally, we studied the benefits of the three types of information to meta-heuristics. Conclusions were drawn from the comprehensive study, which can be used as principles to guide the design of effective meta-heuristic in the future.
报告人简介：孙建永，博士，讲师。分别于1997、1999年在西安交通大学数学系获学士、硕士学位，于2006年在英国艾塞克斯大学获计算机科学博士学位。目前担任英国阿伯泰大学工程、计算与应用数学系讲师。长期从事计算智能、演化优化，随机机器学习研究，以及其在生物信息学及图像处理等领域的应用。近年来也开展了天文信息学与系统生物学的交叉科学研究。已发表学术论文30余篇（其中SCI论文近20篇），论文被引用总次数逾400余次。孙建永博士目前任Open Journal of Artificial Intelligence, Applied Intelligence 等国际学术期刊的编委，是多个国际会议的程序委员会成员 。