Evolutionary many-objective optimisation: an exploratory analysis

Abstract

This inquiry explores the effectiveness of a class of modern evolutionary algorithms, represented by NSGA-II, for solving optimisation tasks with many conflicting objectives. Optimiser behaviour is assessed for a grid of recombination operator configurations. Performance maps are obtained for the dual aims of proximity to, and distribution across, the optimal trade-off surface. Classical settings for recombination are shown to be suitable for small numbers of objectives but correspond to very poor performance as the number of objectives is increased, even when large population sizes are used. Explanations for this behaviour are offered via the concepts of dominance resistance and active diversity promotion.