Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning

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.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 3093-3096
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.3093
• Online since: January 2013
• Authors: Dae Won Kim, Song Ko, Bo Yeong Kang
• Keywords: Bayesian Network (BN), Estimation of Distribution Algorithms, Mutation, Structure Learning

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