Evolving Logic Programs to Classify Chess-Endgame Positions

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{reiser:1998:elpccegp,
  author =       "Philip G. K. Reiser and Patricia J. Riddle",
  title =        "Evolving Logic Programs to Classify Chess-Endgame
                 Positions",
  booktitle =    "Simulated Evolution and Learning: Second Asia-Pacific
                 Conference on Simulated Evolution and Learning,
                 SEAL'98. Selected Papers",
  year =         "1998",
  editor =       "R. I. Bob McKay and X. Yao and Charles S. Newton and 
                 J.-H. Kim and T. Furuhashi",
  volume =       "1585",
  series =       "LNAI",
  pages =        "138--145",
  address =      "Australian Defence Force Academy, Canberra,
                 Australia",
  publisher_address = "Heidelberg",
  month =        "24-27 " # nov,
  publisher =    "Springer-Verlag",
  note =         "published in 1999",
  keywords =     "genetic algorithms, genetic programming, ILP, chess",
  ISSN =         "0302-9743",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1585&spage=138",
  URL =          "http://www.stancomb.co.uk/~prr/Papers/seal98.ps",
  URL =          "http://www.stancomb.co.uk/~prr/Papers/seal98.pdf",
  DOI =          "doi:10.1007/3-540-48873-1_19",
  abstract =     "In this paper, an algorithm is presented for learning
                 concept classification rules. It is a hybrid between
                 evolutionary computing and inductive logic programming
                 (ILP). Given input of positive and negative examples,
                 the algorithm constructs a logic program to classify
                 these examples. The algorithm has several attractive
                 features including the ability to explicitly use
                 background (user-supplied) knowledge and to produce
                 comprehensible output. We present results of using the
                 algorithm to tackle the chess-endgame problem (KRK).
                 The results show that using fitness proportionate
                 selection to bias the population of ILP learners does
                 not significantly increase classification accuracy.
                 However, when rules are exchanged at intermediate
                 stages in learning, in a manner similar to crossover in
                 Genetic Programming, the predictive accuracy is
                 frequently improved.",
  notes =        "SEAL'98 Published as springer-verlag LNAI 1585
                 SEAL98#026 Session 7: Genetic Programming Chair:
                 Sung-Bae Cho, Yonsei Univ., Korea",
}

Genetic Programming entries for Philip G K Reiser Patricia Jean Riddle

Citations