Evolving Lose-Checkers Players using Genetic Programming

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

  author =       "Amit Benbassat and Moshe Sipper",
  title =        "Evolving Lose-Checkers Players using Genetic
  booktitle =    "IEEE Conference on Computational Intelligence and
  year =         "2010",
  pages =        "30--37",
  address =      "IT University of Copenhagen, Denmark",
  month =        "18-21 " # aug,
  keywords =     "genetic algorithms, genetic programming, explicitly
                 defined intron, full knowledge board game, genetic
                 programming tree, local mutation, lose checker player,
                 multitree individual, state evaluator, computer games,
                 trees (mathematics)",
  URL =          "http://game.itu.dk/cig2010/proceedings/papers/cig10_005_011.pdf",
  DOI =          "doi:10.1109/ITW.2010.5593376",
  size =         "8 pages",
  abstract =     "We present the application of genetic programming (GP)
                 to the zero-sum, deterministic, full-knowledge board
                 game of Lose Checkers. Our system implements strongly
                 typed GP trees, explicitly defined introns, local
                 mutations, and multitree individuals. Explicitly
                 defined introns in the genome allow for information
                 selected out of the population to be kept as a
                 reservoir for possible future use. Multi-tree
                 individuals are implemented by a method inspired by
                 structural genes in living organisms, whereby we take a
                 single tree describing a state evaluator and split
  notes =        "http://game.itu.dk/cig2010/proceedings/wp-content/acceptedpapers.html
                 Also known as \cite{5593376}",

Genetic Programming entries for Amit Benbassat Moshe Sipper