Evolutionary Multiobjective Optimization and Multiobjective Knowledge Extraction

Abstract

Evolutionary multiobjective optimization (EMO) has been one of the most active research areas in the field of evolutionary computation for the last decade. In this paper, first we overview a number of representative EMO algorithms. Next we explain the common framework of well-known and frequently used EMO algorithms such as NSGA-II and SPEA. Then we discuss hot issues in the EMO community. Finally we explain how EMO algorithms can be applied to multiobjective knowledge extraction. As an example, we show experimental results of EMO algorithms for multiobjective design of fuzzy rule-based classifiers where a number of non-dominated classifiers are obtained by a single run of an EMO algorithm.