PCFG-LA混合モデルに基づく分布推定アルゴリズム  [in Japanese] Estimation of Distribution Algorithm Based on PCFG-LA Mixture Model  [in Japanese]

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Author(s)

    • 長谷川 禎彦 Hasegawa Yoshihiko
    • 東京大学大学院新領域創成科学研究科情報生命科学専攻 Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo
    • 伊庭 斉志 Iba Hitoshi
    • 東京大学大学院工学系研究科電気系工学専攻 Department of Electorical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo

Abstract

Estimation of distribution algorithms (EDAs) are evolutionary algorithms which substitute traditional genetic operators with distribution estimation and sampling. Recently, the application of probabilistic techniques to program and function evolution has received increasing attention, and promises to provide a strong alternative to the traditional genetic programming (GP) techniques. Although PAGE (Programming with Annotated Grammar Estimation) is a state-of-art GP-EDA based on PCFG-LA (PCFG with Latent Annotations), PAGE can not effectively estimate the distribution with multiple solutions. In this paper, we proposed extended PCFG-LA named PCFG-LAMM (PCFG-LA Mixture Model) and proposed UPAGE (Unsupervised PAGE) based on PCFG-LAMM. By applying the proposed algorithm to three computational problems, it is demonstrated that our approach requires fewer fitness evaluations. We also show that UPAGE is capable of obtaining multiple solutions in a multimodal problem.

Journal

  • Transactions of the Japanese Society for Artificial Intelligence

    Transactions of the Japanese Society for Artificial Intelligence 24(1), 80-91, 2009

    The Japanese Society for Artificial Intelligence

Codes

  • NII Article ID (NAID)
    130000098272
  • Text Lang
    JPN
  • ISSN
    1346-0714
  • Data Source
    J-STAGE 
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