An extended probabilistic model building genetic network programming using both of good and bad individuals

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@Article{Li:2013:TEEE,
  author =       "Xianneng Li and Shingo Mabu and Kotaro Hirasawa",
  title =        "An extended probabilistic model building genetic
                 network programming using both of good and bad
                 individuals",
  journal =      "IEEJ Transactions on Electrical and Electronic
                 Engineering",
  year =         "2013",
  volume =       "8",
  number =       "4",
  pages =        "339--347",
  month =        jul,
  publisher =    "Wiley",
  keywords =     "genetic algorithms, genetic programming, probabilistic
                 modelling, estimation of distribution algorithms
                 (EDAs), bad individuals, reinforcement learning,
                 probabilistic model building genetic network
                 programming",
  ISSN =         "1931-4981",
  DOI =          "doi:10.1002/tee.21864",
  size =         "9 pages",
  abstract =     "Classical estimation of distribution algorithms (EDAs)
                 generally use truncation selection to estimate the
                 distribution of the good individuals while ignoring the
                 bad ones. However, various researches in evolutionary
                 algorithms (EAs) have reported that the bad individuals
                 may affect and help solving the problem. This paper
                 proposes a new method to use the bad individuals by
                 studying the substructures rather than the entire
                 individual structures to solve reinforcement learning
                 (RL) problems, which generally factorise 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 could solve RL problems
                 successfully, to propose an extended PMBGNP. The
                 effectiveness of this work is verified in an RL
                 problem, namely robot control. Compared to other
                 related work, results show that the proposed method can
                 significantly speed up the evolution efficiency.",
}

Genetic Programming entries for Xianneng Li Shingo Mabu Kotaro Hirasawa

Citations