A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

• Autores: Sho Fukuda, Y. Yamanaka, T. Yoshihiro
• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 3, Nº. 1, 2014, págs. 7-13
• Idioma: inglés
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• Resumen

  • Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning), and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks.