Visiting from Japan to deliver this IEEE Distinguished Lecture, Professor Hisao Ishibuchi will discuss recent research topics in evolutionary multi-objective and many-objective optimisation.
Recently, evolutionary many-objective optimisation has been one of the most active research areas in the field of evolutionary computation. A current trend is the use of a set of well-distributed weight vectors (reference directions, reference points) for many-objective optimisation in a similar framework to the decomposition-based EMO algorithm (i.e., MOEA/D).
In this presentation, we will discuss some recent research topics in the field of evolutionary multi-objective and many-objective optimisation. Emphasis will be placed on the difficulty in performance comparison of evolutionary many-objective algorithms from the following viewpoints:
- Population size specification for fair comparison,
- Special characteristic features of frequently used many-objective test problems called DTLZ and WFG,
- Dependency of the performance of recently-proposed MODE/D-based many-objective algorithms on the shape of the Pareto fronts of those test problems, and
- Dependency of hypervolume-based performance comparison results on the choice of a reference point for hypervolume calculation.
About the speaker
Professor Hisao Ishibuchi is based in the Graduate School of Engineering, Osaka Prefecture University, Japan, with research interests including fuzz rule-based classifier design, evolutionary multi-objective and many-objective optimisation, and evolutionary games.
As well as numerous best paper awards from conferences around the world, he has received a 2007 JSPS Prize. He is an IEEE fellow and the Editor-in-Chief of IEEE CI Magazine (2014-2017), an IEEE CIS AdCom member (2014-2016), and an IEEE CIS Distinguished Lecturer (2015-2017). More information about Professor Ishibuchi and his research.
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