Effective linkage learning using low-order statistics and clustering

TL;DR

This paper introduces a novel combination operator guided by information theory that enhances low-order EDAs with clustering, enabling effective solutions across diverse benchmark optimization problems.

Contribution

It proposes a new combination operator that improves low-order EDAs using clustering and information-theoretic measures, addressing limitations in solving complex problems.

Findings

Enhanced performance on benchmark problems

Effective integration of clustering with low-order EDAs

Improved ability to find global optima

Abstract

The adoption of probabilistic models for the best individuals found so far is a powerful approach for evolutionary computation. Increasingly more complex models have been used by estimation of distribution algorithms (EDAs), which often result better effectiveness on finding the global optima for hard optimization problems. Supervised and unsupervised learning of Bayesian networks are very effective options, since those models are able to capture interactions of high order among the variables of a problem. Diversity preservation, through niching techniques, has also shown to be very important to allow the identification of the problem structure as much as for keeping several global optima. Recently, clustering was evaluated as an effective niching technique for EDAs, but the performance of simpler low-order EDAs was not shown to be much improved by clustering, except for some simple multimodal problems. This work proposes and evaluates a combination operator guided by a measure from information theory which allows a clustered low-order EDA to effectively solve a comprehensive range of benchmark optimization problems.