文章基本信息

标题：Probabilistic Model Building Genetic Programming based on Estimation of Bayesian Network
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作者：Yoshihiko Hasegawa ; Hitoshi Iba
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2007
卷号：22
期号：1
页码：37-47
DOI：10.1527/tjsai.22.37
出版社：The Japanese Society for Artificial Intelligence
摘要：Genetic Programming (GP) is a powerful optimization algorithm, which employs the crossover for genetic operation. Because the crossover operator in GP randomly selects sub-trees, the building blocks may be destroyed by the crossover. Recently, algorithms called PMBGPs (Probabilistic Model Building GP) based on probabilistic techniques have been proposed in order to improve the problem mentioned above. We propose a new PMBGP employing Bayesian network for generating new individuals with a special chromosome called expanded parse tree , which much reduces a number of possible symbols at each node. Although the large number of symbols gives rise to the large conditional probability table and requires a lot of samples to estimate the interactions among nodes, a use of the expanded parse tree overcomes these problems. Computational experiments on two subjects demonstrate that our new PMBGP is much superior to prior probabilistic models.
关键词：POLE ; genetic programming ; probabilistic model building ; Bayesian network ; expanded parse tree ; DMAX problem