文章基本信息

标题：Binary Encoded-Prototype Tree for Probabilistic Model Building GP
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作者：Toshihiko Yanase ; Yoshihiko Hasegawa ; Hitoshi Iba 等
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：2010
卷号：25
期号：2
页码：340-350
DOI：10.1527/tjsai.25.340
出版社：The Japanese Society for Artificial Intelligence
摘要：In recent years, program evolution algorithms based on the estimation of distribution algorithm (EDA) have been proposed to improve search ability of genetic programming (GP) and to overcome GP-hard problems. One such method is the probabilistic prototype tree (PPT) based algorithm. The PPT based method explores the optimal tree structure by using the full tree whose number of child nodes is maximum among possible trees. This algorithm, however, suffers from problems arising from function nodes having different number of child nodes. These function nodes cause intron nodes, which do not affect the fitness function. Moreover, the function nodes having many child nodes increase the search space and the number of samples necessary for properly constructing the probabilistic model. In order to solve this problem, we propose binary encoding for PPT. In this article, we convert each function node to a subtree of binary nodes where the converted tree is correct in grammar. Our method reduces ineffectual search space, and the binary encoded tree is able to express the same tree structures as the original method. The effectiveness of the proposed method is demonstrated through the use of two computational experiments.
关键词：estimation of distribution algorithm ; probabilistic prototype tree ; genetic programming ; representation