Developing neural structure of two agents that play checkers using cartesian genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Gul Muhammad Khan and Julian Francis Miller and 
                 David M. Halliday",
  title =        "Developing neural structure of two agents that play
                 checkers using cartesian genetic programming",
  year =         "2008",
  editor =       "Marc Ebner and Mike Cattolico and 
                 Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and 
                 Frank W. Moore and Clare Bates Congdon and 
                 Christopher D. Clack and Frank W. Moore and William Rand and 
                 Sevan G. Ficici and Rick Riolo and Jaume Bacardit and 
                 Ester Bernado-Mansilla and Martin V. Butz and 
                 Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and 
                 Martin Pelikan and Kumara Sastry",
  isbn13 =       "978-1-60558-131-6",
  booktitle =    "GECCO-2008 Late-Breaking Papers",
  pages =        "2169--2174",
  address =      "Atlanta, GA, USA",
  URL =          "",
  DOI =          "doi:10.1145/1388969.1389042",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "12-16 " # jul,
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, artificial neural networks,
                 checkers, co-evolution, computational development",
  abstract =     "A developmental model of neural network is presented
                 and evaluated in the game of Checkers. The network is
                 developed using Cartesian genetic programs (CGP) as
                 genotypes. Two agents are provided with this network
                 and allowed to co-evolve until they start playing
                 better. The network that occurs by running theses
                 genetic programs has a highly dynamic morphology in
                 which neurons grow, and die, and neurite branches
                 together with synaptic connections form and change in
                 response to situations encountered on the checkers
                 board. The method has no board evaluation function, no
                 explicit learning rules and no human expertise at
                 playing checkers is used. The results show that, after
                 a number of generations, by playing each other the
                 agents begin to play much better and can easily beat
                 agents that occur in earlier generations. Such learning
                 abilities are encoded at a genetic level rather than at
                 the phenotype level of neural connections.",
  notes =        "Distributed on CD-ROM at GECCO-2008

                 ACM Order Number 910081. Also known as \cite{1389042}",

Genetic Programming entries for Gul Muhammad Khan Julian F Miller David M Halliday