Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

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

@Article{Preen:2013:SC,
  author =       "Richard J. Preen and Larry Bull",
  title =        "Discrete and fuzzy dynamical genetic programming in
                 the XCSF learning classifier system",
  journal =      "Soft Computing",
  year =         "2014",
  volume =       "18",
  number =       "1",
  pages =        "153--167",
  month =        jan,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Fuzzy logic
                 networks, Learning classifier systems, Memory, Random
                 Boolean networks, Reinforcement learning,
                 Self-adaptation, XCSF",
  ISSN =         "1432-7643",
  URL =          "http://arxiv.org/abs/1201.5604",
  DOI =          "doi:10.1007/s00500-013-1044-4",
  language =     "English",
  size =         "15 pages",
  abstract =     "A number of representation schemes have been presented
                 for use within learning classifier systems, ranging
                 from binary encodings to neural networks. This paper
                 presents results from an investigation into using
                 discrete and fuzzy dynamical system representations
                 within the XCSF learning classifier system. In
                 particular, asynchronous random Boolean networks are
                 used to represent the traditional condition-action
                 production system rules in the discrete case and
                 asynchronous fuzzy logic networks in the
                 continuous-valued case. It is shown possible to use
                 self-adaptive, open-ended evolution to design an
                 ensemble of such dynamical systems within XCSF to solve
                 a number of well-known test problems.",
  notes =        "See also \cite{oai:arXiv.org:1201.5604}",
}

Genetic Programming entries for Richard Preen Larry Bull

Citations