Evolving both search and strategy for Reversi players using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

  author =       "Amit Benbassat and Moshe Sipper",
  title =        "Evolving both search and strategy for Reversi players
                 using genetic programming",
  booktitle =    "IEEE Conference on Computational Intelligence and
                 Games, CIG 2012",
  year =         "2012",
  pages =        "47--54",
  address =      "Granada",
  month =        "11-14 " # sep,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, computer
                 games, search problems, trees (mathematics), Reversi
                 players, deterministic board game, full-knowledge board
                 game, game-tree pruning, search algorithm, selective
                 directional crossover method, zero-sum board game,
                 Games, Humans, Receivers, Sociology, Statistics",
  isbn13 =       "978-1-4673-1193-9",
  DOI =          "doi:10.1109/CIG.2012.6374137",
  size =         "8 pages",
  abstract =     "We present the application of genetic programming to
                 the zero-sum, deterministic, full-knowledge board game
                 of Reversi. Expanding on our previous work on evolving
                 boardstate evaluation functions, we now evolve the
                 search algorithm as well, by allowing evolved programs
                 control of game-tree pruning. We use strongly typed
                 genetic programming, explicitly defined introns, and a
                 selective directional crossover method. We show that
                 our system regularly churns out highly competent
                 players and our results prove easy to scale.",
  notes =        "Also known as \cite{6374137}",

Genetic Programming entries for Amit Benbassat Moshe Sipper