Dynamical Genetic Programming in XCSF

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Richard J. Preen and Larry Bull",
  title =        "Dynamical Genetic Programming in {XCSF}",
  journal =      "Evolutionary Computation",
  year =         "2013",
  volume =       "21",
  number =       "3",
  pages =        "361--387",
  month =        "Fall",
  keywords =     "genetic algorithms, genetic programming, Graph-based
                 genetic programming, learning classifier systems,
                 multistep-ahead prediction, reinforcement learning,
                 self-adaptation, symbolic regression, XCSF, LCS",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/EVCO_a_00080",
  URL =          "http://results.ref.ac.uk/Submissions/Output/502034",
  size =         "27 pages",
  abstract =     "A number of representation schemes have been presented
                 for use within learning classifier systems, ranging
                 from binary encodings to artificial neural networks.
                 This paper presents results from an investigation into
                 using a temporally dynamic symbolic representation
                 within the XCSF learning classifier system. In
                 particular, dynamical arithmetic networks are used to
                 represent the traditional condition-action production
                 system rules to solve continuous-valued reinforcement
                 learning problems and to perform symbolic regression,
                 finding competitive performance with traditional
                 genetic programming on a number of composite polynomial
                 tasks. In addition, the network outputs are later
                 repeatedly sampled at varying temporal intervals to
                 perform multistep-ahead predictions of a financial time
  uk_research_excellence_2014 = "Knowledge representation and reasoning.
                 This paper presents the first known approach to using
                 the inherent dynamical behaviour of artficial genetic
                 regulatory networks to predict the temporal dynamics of
                 time series data.",

Genetic Programming entries for Richard Preen Larry Bull