报告题目：Improving Metaheuristic Performance by Evolving Multi-objective Functions
报告时间：2018年10月29日上午10:00
报告地点：尚贤楼706
主持人：陈允杰教授
专家简介：
Keshav P. Dahal is a Professor of Intelligent Systems and the leader of the Artificial Intelligence, Visual Communication and Network (AVCN) Research Centre at the University of the West of Scotland (UWS), UK and also a visiting professor of Nanjing University of Information Science and Technology (NUIST) China. He received the M.S. degree in electrical power engineering and the Ph.D. degree from the Department of Electronic and Electrical, University of Strathclyde, Glasgow, U.K., in 1996 and 2000, respectively. Before joining UWS he was with Bradford and Strathclyde Universities in U.K. He has published over 130 journal and conference papers with some award winning papers and has sat on organizing/program committees of more than 55 international conferences including as the General Chair and Programmed Chair. His research interests lie in the areas of applied AI to intelligent systems, trust and security modeling in distributed systems, and scheduling/optimization problems.
报告简介：
This talk will present some of the recent development in multi-objective approaches for solving complex scheduling problem. The first part of the talk will investigate multi-objective and weighted single objective approaches to a real world workforce scheduling problem. We show that multi-objective genetic algorithms can create solutions whose fitness is close to that of the solution created by the genetic algorithms using weighted sum objectives even though the multi-objective approaches know nothing of the weights. In second part of the talk will discuss the variable fitness function approach to enhance the metaheuristic approaches by evolving weights for each of the multiple objectives. We show that the variable fitness function approach significantly improves performance of constructive and variable neighbourhood search (VNS) approaches on workforce scheduling problem instances.
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数学与统计学院
2018年10月23日
