Sub-structural Niching in Estimation of Distribution Algorithms

TL;DR

This paper introduces a sub-structural niching method that enhances diversity preservation in estimation of distribution algorithms by exploiting problem decomposition, leading to better maintenance of multiple optima with fewer resources.

Contribution

It presents a novel sub-structural niching approach that leverages problem decomposition for improved diversity preservation in EDAs, outperforming traditional methods like RTS.

Findings

Successfully maintains multiple global optima over many generations.

Uses significantly fewer individuals than RTS.

Achieves more accurate niche market share distribution.

Abstract

We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation of distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The sub-structural niching method is compared to restricted tournament selection (RTS)--a niching method used in hierarchical Bayesian optimization algorithm--with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS.