An Evaluation of Evolutionary Generalisation in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{kushchu:2002:AIR,
  author =       "Ibrahim Kushchu",
  title =        "An Evaluation of Evolutionary Generalisation in
                 Genetic Programming",
  journal =      "Artificial Intelligence Review",
  year =         "2002",
  volume =       "18",
  number =       "1",
  pages =        "3--14",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, learning,
                 robustness",
  DOI =          "doi:10.1023/A:1016379201230",
  abstract =     "Generalisation is one of the most important
                 performance evaluation criteria for artificial learning
                 systems. An increasing amount of research has recently
                 concentrated on the robustness or generalisation
                 ability of the programs evolved using Genetic
                 Programming (GP). While some of these researchers
                 report on the brittleness of the solutions evolved,
                 some others propose methods of promoting
                 robustness/generalisation. In this paper, a review of
                 research on generalisation in GP and problems with
                 brittleness of solutions produced by GP is presented.
                 Also, a brief overview of several new methods promoting
                 robustness/generalisation of the solutions produced by
                 GP are presented.",
  notes =        "Article ID: 405648

                 Note author previously spelt name as I Kuscu",
}

Genetic Programming entries for Ibrahim Kuscu

Citations