Chair: Hiroyuki Masuta
Title: Introduction to Evolutionary Multiobjective Optimization
Department of Computer Science and Intelligent Systems
Osaka Prefecture University, Japan
Talk Abstract：
Real-world optimization problems often include multiple objective functions to be optimized at the same time. If all/some of objective functions are conflicting among others, there exists no single optimal solution. There are a number of Pareto optimal solutions with different tradeoffs among objectives. Evolutionary multiobjective optimization utilizes a population-based search ability to efficiently find a number of Pareto or near-Pareto optimal solutions by its single run. This lecture introduces the basic idea of evolutionary multiobjective optimization algorithms, the quality indicators, and some recent topics including many-objective optimization, large scale optimization, evolutionary multitasking, and innovization.
Vita：
Yusuke Nojima received the B.S. and M.S. Degrees in mechanical engineering from Osaka Institute of Technology, Osaka, Japan, in 1999 and 2001, respectively, and the Ph.D. degree in system function science from Kobe University, Hyogo, Japan, in 2004. Since 2004, he has been with Osaka Prefecture University, Osaka, Japan, where he was a Research Associate and is currently an Associate Professor in Department of Computer Science and Intelligent Systems. His research interests include evolutionary fuzzy systems, evolutionary multiobjective optimization, and parallel distributed data mining. He was a guest editor for several special issues in international journals. He was a task force chair on Evolutionary Fuzzy Systems in Fuzzy Systems Technical Committee of IEEE Computational Intelligence Society. He was an associate editor of IEEE Computational Intelligence Magazine (2014-2019).
Invited Lecture：