Continuous probabilistic model building genetic network programming using reinforcement learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Li:2015:ASC,
  author =       "Xianneng Li and Kotaro Hirasawa",
  title =        "Continuous probabilistic model building genetic
                 network programming using reinforcement learning",
  journal =      "Applied Soft Computing",
  year =         "2015",
  volume =       "27",
  number =       "Supplement C",
  pages =        "457--467",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, Estimation of distribution
                 algorithm, Probabilistic model building, Continuous
                 optimization, Reinforcement learning",
  ISSN =         "1568-4946",
  URL =          "http://www.sciencedirect.com/science/article/pii/S156849461400533X",
  DOI =          "doi:10.1016/j.asoc.2014.10.023",
  abstract =     "Recently, a novel probabilistic model-building
                 evolutionary algorithm (so called estimation of
                 distribution algorithm, or EDA), named probabilistic
                 model building genetic network programming (PMBGNP),
                 has been proposed. PMBGNP uses graph structures for its
                 individual representation, which shows higher
                 expression ability than the classical EDAs. Hence, it
                 extends EDAs to solve a range of problems, such as data
                 mining and agent control. This paper is dedicated to
                 propose a continuous version of PMBGNP for continuous
                 optimization in agent control problems. Different from
                 the other continuous EDAs, the proposed algorithm
                 evolves the continuous variables by reinforcement
                 learning (RL). We compare the performance with several
                 state-of-the-art algorithms on a real mobile robot
                 control problem. The results show that the proposed
                 algorithm outperforms the others with statistically
                 significant differences.",
}

Genetic Programming entries for Xianneng Li Kotaro Hirasawa

Citations