Solving the 8-puzzle problem using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Kevin Igwe and Nelishia Pillay and Christopher Rae",
  title =        "Solving the 8-puzzle problem using genetic
  booktitle =    "Proceedings of the South African Institute for
                 Computer Scientists and Information Technologists
                 Conference, SAICSIT'13",
  year =         "2013",
  editor =       "John McNeill and Karen L. Bradshaw and 
                 Philip Machanick and Mosiuoa Tsietsi",
  pages =        "64--67",
  address =      "East London, South Africa",
  month =        oct # " 7-9",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, game",
  bibdate =      "2013-09-28",
  bibsource =    "DBLP,
  isbn13 =       "978-1-4503-2112-9",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2513456.2513492",
  acmid =        "2513492",
  size =         "4 pages",
  abstract =     "The 8-puzzle problem is a classic artificial
                 intelligence problem which has been well-researched.
                 The research in this domain has focused on evaluating
                 traditional search methods such as the breadth-first
                 search and the A* algorithm and deriving and testing
                 various heuristics for use with informed searches to
                 solve the 8-puzzle problem. The study presented in this
                 paper evaluates a machine learning technique, namely
                 genetic programming, as means of solving the 8-puzzle
                 problem. The genetic programming algorithm uses the
                 grow method to create an initial population which is
                 iteratively refined using tournament selection to
                 choose parents which the reproduction, mutation and
                 crossover operators are applied to, thereby producing
                 successive generations. The edit operator has been used
                 to exert parsimony pressure in order to reduce the size
                 of solution trees and hence the number of moves to
                 solve a problem instance. The genetic programming
                 system was successfully applied to 20 problem instances
                 of differing difficulty, producing solutions to all 20
                 problems. Furthermore, for a majority of the problems
                 the solutions produced solve the problem instance using
                 the known minimum number of moves.",
  notes =        "Tree GP. Comparison between: depth first, breadth
                 first, A* (A-star) and

        Also known as

Genetic Programming entries for Kevin C Igwe Nelishia Pillay Christopher Rae