ベイジアンネットワーク推定による確率モデル遺伝的プログラミング

• 長谷川 禎彦

  東京大学大学院新領域創成科学研究科

• 伊庭 斉志

  東京大学大学院新領域創成科学研究科

書誌事項

タイトル別名

• Probabilistic Model Building Genetic Programming based on Estimation of Bayesian Network
• ベイジアン ネットワーク スイテイ ニ ヨル カクリツ モデル イデンテキ プログラミング

抄録

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.

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 22 (1), 37-47, 2007

  一般社団法人 人工知能学会

キーワード

• POLE
• genetic programming
• probabilistic model building
• Bayesian network
• expanded parse tree
• DMAX problem

詳細情報

• CRID: 1390001205108944640
• NII論文ID: 10022007013
• NII書誌ID: AA11579226
• DOI: 10.1527/tjsai.22.37
• ISSN: 13468030
• ISSN: 13460714
• NDL書誌ID: 9603180
• Web Site: http://id.ndl.go.jp/bib/9603180
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I9603180
• Web Site: http://www.jstage.jst.go.jp/article/tjsai/22/1/22_1_37/_pdf
• 本文言語コード: ja
• データソース種別:
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  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可