A Novel Graph-Based Estimation of the Distribution Algorithm and its Extension Using Reinforcement Learning

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@Article{Li:2014:ieeeTEC,
  author =       "Xianneng Li and Shingo Mabu and Kotaro Hirasawa",
  title =        "A Novel Graph-Based Estimation of the Distribution
                 Algorithm and its Extension Using Reinforcement
                 Learning",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2014",
  volume =       "18",
  number =       "1",
  pages =        "98--113",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, Agent control, estimation of
                 distribution algorithm (EDA), GNP, graph structure,
                 reinforcement learning (RL)",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2013.2238240",
  size =         "16 pages",
  abstract =     "In recent years, numerous studies have drawn the
                 success of estimation of distribution algorithms (EDAs)
                 to avoid the frequent breakage of building blocks of
                 the conventional stochastic genetic operators-based
                 evolutionary algorithms (EAs). In this paper, a novel
                 graph-based EDA called probabilistic model building
                 genetic network programming (PMBGNP) is proposed. Using
                 the distinguished graph (network) structure of a
                 graph-based EA called genetic network programming
                 (GNP), PMBGNP ensures higher expression ability than
                 the conventional EDAs to solve some specific problems.
                 Furthermore, an extended algorithm called reinforced
                 PMBGNP is proposed to combine PMBGNP and reinforcement
                 learning to enhance the performance in terms of fitness
                 values, search speed, and reliability. The proposed
                 algorithms are applied to solve the problems of
                 controlling the agents' behaviour. Two problems are
                 selected to demonstrate the effectiveness of the
                 proposed algorithms, including the benchmark one, i.e.,
                 the Tileworld system, and a real mobile robot
                 control.",
  notes =        "also known as \cite{6408015}",
}

Genetic Programming entries for Xianneng Li Shingo Mabu Kotaro Hirasawa

Citations