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  • 标题:Probabilistic Model Building Genetic Network Programming Using Reinforcement Learning
  • 本地全文:下载
  • 作者:Xianneng Li ; Shingo Mabu ; Bing Li
  • 期刊名称:進化計算学会論文誌
  • 电子版ISSN:2185-7385
  • 出版年度:2011
  • 卷号:2
  • 期号:1
  • 页码:29-40
  • DOI:10.11394/tjpnsec.2.29
  • 出版社:The Japanese Society for Evolutionary Computation
  • 摘要:

    This paper proposed a novel Estimation of Distribution Algorithm (EDA), where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., mobile robot control. The experimental results show the superiority of the proposed algorithm over conventional algorithms by comparing the quality and generalization ability of the solutions.

  • 关键词:probabilistic modeling; estimation of distribution algorithm (EDA); genetic network programming (GNP); reinforcement learning; probabilistic model building genetic network programming (PMBGNP)
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