On dynamical genetic programming: simple Boolean networks in learning classifier systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Larry Bull",
  title =        "On dynamical genetic programming: simple {Boolean}
                 networks in learning classifier systems",
  journal =      "International Journal of Parallel, Emergent and
                 Distributed Systems",
  year =         "2009",
  volume =       "24",
  number =       "5",
  pages =        "421--442",
  month =        oct,
  publisher =    "Taylor \& Francis",
  keywords =     "genetic algorithms, genetic programming, discrete,
                 dynamical systems, evolution, multiplexer, unorganised
  ISSN =         "1744-5760",
  DOI =          "doi:10.1080/17445760802660387",
  abstract =     "Many representations have been presented to enable the
                 effective evolution of computer programs. Turing was
                 perhaps the first to present a general scheme by which
                 to achieve this end. Significantly, Turing proposed a
                 form of discrete dynamical system and yet dynamical
                 representations remain almost unexplored within
                 conventional genetic programming (GP). This paper
                 presents results from an initial investigation into
                 using simple dynamical GP representations within a
                 learning classifier system. It is shown possible to
                 evolve ensembles of dynamical Boolean function networks
                 to solve versions of the well-known multiplexer
                 problem. Both synchronous and asynchronous systems are
  notes =        "a Department of Computer Science, University of the
                 West of England, Bristol, UK Formerly Parallel
                 Algorithms and Applications",

Genetic Programming entries for Larry Bull