Use of infeasible individuals in Probabilistic Model Building Genetic Network Programming

Xianneng Li*, Shingo Mabu, Kotaro Hirasawa

*この研究の対応する著者

研究成果: Conference contribution

15 被引用数 (Scopus)

抄録

Classical EDAs generally use truncation selection to estimate the distribution of the feasible (good) individuals while ignoring the infeasible (bad) ones. However, various research in EAs reported that the infeasible individuals may affect and help the problem solving. This paper proposed a new method to use the infeasible individuals by studying the sub-structures rather than the entire individual structures to solve Reinforcement Learning (RL) problems, which generally factorize their entire solutions to the sequences of state-action pairs. This work was studied in a recent graph-based EDA named Probabilistic Model Building Genetic Network Programming (PMBGNP) which can solve RL problems successfully. The effectiveness of this work is verified in a RL problem, i.e., robot control, comparing with some other related work.

本文言語English
ホスト出版物のタイトルGenetic and Evolutionary Computation Conference, GECCO'11
ページ601-608
ページ数8
DOI
出版ステータスPublished - 2011
イベント13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin
継続期間: 2011 7 122011 7 16

Other

Other13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
CityDublin
Period11/7/1211/7/16

ASJC Scopus subject areas

  • 計算理論と計算数学
  • 理論的コンピュータサイエンス

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