Evolutionary many-objective optimization

Abstract

In this paper, we first explain why many-objective problems are difficult for Pareto dominance-based evolutionary multiobjective optimization algorithms such as NSGA-II and SPEA. Then we explain recent proposals for the handling of many-objective problems by evolutionary algorithms. Some proposals are examined through computational experiments on multiobjective knapsack problems with two, four and six objectives. Finally we discuss the viability of many-objective genetic fuzzy systems (i.e., the use of many-objective genetic algorithms for the design of fuzzy rule-based systems).