A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks
TL;DR: This paper targets on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning), and proposes a new mutation operator that has a good performance in learning Bayesian networks.
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Abstract: 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.
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