Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning

TL;DR

This paper introduces a novel matrix transpose mutation operator for estimation of distribution algorithms, significantly improving Bayesian structure learning performance compared to traditional EDAs.

Contribution

It proposes a new transpose mutation operator tailored for Bayesian structure learning within EDAs, demonstrating enhanced effectiveness.

Findings

Transpose mutation outperforms conventional EDAs

Improved optimization performance in Bayesian learning

Enhanced population diversity in EDAs

Abstract

Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms, providing effective and efficient optimization performance in a variety of research areas. Recent studies have proposed new EDAs that employ mutation operators in standard EDAs to increase the population diversity. We present a new mutation operator, a matrix transpose, specifically designed for Bayesian structure learning, and we evaluate its performance in Bayesian structure learning. The results indicate that EDAs with transpose mutation give markedly better performance than conventional EDAs.