01334nas a2200217   4500000000100000000000100001008004100002260001200043653002100055653002700076653000900103100001500112700001900127700002100146245008900167856009800256300000900354490000600363520073300369022001401102       2014                            d  c12/201410aBayesian Network10aEvolutionary Algorithm10aPBIL1 aSho Fukuda1 aYuuma Yamanaka1 aTakuya Yoshihiro00aA Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks  uhttp://www.ijimai.org/journal/sites/default/files/files/2014/11/ijimai20143_1_1_pdf_24199.pdf  a7-130 v33 aBayesian 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  a1989-1660