Hierarchical importance sampling as generalized population convergence (教理モデル化と問題解決)

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概要

• 論文の詳細を見る

• This paper proposes a novel method, named Hierarchical Importance Sampling (HIS), as a generalization of the population convergence, which plays an important role in Optimization Methods based on Probability Models (OMPM) such as Estimation of Distribution Algorithms and Cross Entropy methods. In HIS, multiple populations are maintained simultaneously so that they have different diversities. Experimental comparisons between HIS and general OMPM have revealed that HIS outperforms general OMPM.

• 一般社団法人情報処理学会の論文
• 2007-09-03

著者

• Takadama Keiki

  The University Of Electro-communications Faculty Of Electro-communication

• Higo Takayuki

  Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology

• Higo Takayuki

  Graduate School Of Interdisciplinary Science And Engineering Tokyo Institute Of Technology

関連論文

• Hierarchical importance sampling as generalized population convergence (教理モデル化と問題解決)

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