Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{andre:1996:GKL,
  author =       "David Andre and Forrest H {Bennett III} and 
                 John R. Koza",
  title =        "Evolution of Intricate Long-Distance Communication
                 Signals in Cellular Automata using Genetic
                 Programming",
  booktitle =    "Artificial Life V: Proceedings of the Fifth
                 International Workshop on the Synthesis and Simulation
                 of Living Systems",
  year =         "1996",
  volume =       "1",
  address =      "Nara, Japan",
  publisher_address = "Cambridge, MA, USA",
  month =        "16--18 " # may,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf",
  size =         "10 pages",
  abstract =     "A cellular automata rule for the majority
                 classification task was evolved using genetic
                 programming with automatically defined functions. The
                 genetically evolved rule has an accuracy of 82.326%.
                 This level of accuracy exceeds that of the
                 Gacs-Kurdyumov-Levin (GKL) rule, all other known
                 human-written rules, and all other rules produced by
                 known previous automated approaches.

                 Our genetically evolved rule is qualitatively different
                 from other rules in that it uses a fine-grained
                 internal representation of density information; it
                 employs a large number of different domains and
                 particles; and it uses an intricate set of signals for
                 communicating information over large distances in time
                 and space.",
  notes =        "Alife-5 A longer version of this paper will be
                 presented at the GP-96 conference. GP gets best
                 solution to GKL problem

                 {"}The population size used to evolve the current
                 world's record for the GKL majority classification
                 1-dimensionall 2-sate 7-neighbor cellular authomata
                 problem was 51,200.

                 I believe Melanie Mitchell at the Santa Fe Institute
                 has been doing continuing additional work on using GAs
                 to evolve CA rules for various other problems.{"}",
}

Genetic Programming entries for David Andre Forrest Bennett John Koza

Citations