潜在アノテーション推定を用いた確率文法による分布推定アルゴリズム

• 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

• Estimation of Distribution Algorithm Based on Probabilistic Grammar with Latent Annotations

抄録

Evolutionary algorithms (EAs) are optimization methods and are based on the concept of natural evolution. Recently, growing interests has been observed on applying estimation of distribution techniques to EAs (EDAs). Although probabilistic context free grammar (PCFG) is a widely used model in EDAs for program evolution, it is not able to estimate the building blocks from promising solutions because it takes advantage of the context freedom assumption. We have proposed a new program evolution algorithm based on PCFG with latent annotations which weaken the context freedom assumption. Computational experiments on two subjects (the royal tree problem and the DMAX problem) demonstrate that our new approach is highly effective compared to prior approaches including the conventional GP.

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 23 (1), 13-26, 2008

  一般社団法人 人工知能学会

キーワード

• PAGE
• PCFG with latent annotations
• genetic programming
• estimation of distribution algorithm

詳細情報

• CRID: 1390282680083430784
• NII論文ID: 130000097880
• DOI: 10.1527/tjsai.23.13
• ISSN: 13468030
• ISSN: 13460714
• Web Site: http://www.jstage.jst.go.jp/article/tjsai/23/1/23_1_13/_pdf
• 本文言語コード: ja
• データソース種別:
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  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可