Decreasing the Number of Evaluations in Evolutionary Algorithms by using a Meta-Model of the Fitness Function

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{ziegler03,
  author =       "Jens Ziegler and Wolfgang Banzhaf",
  title =        "Decreasing the Number of Evaluations in Evolutionary
                 Algorithms by using a Meta-Model of the Fitness
                 Function",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2003",
  year =         "2003",
  editor =       "Conor Ryan and Terence Soule and Maarten Keijzer and 
                 Edward Tsang and Riccardo Poli and Ernesto Costa",
  volume =       "2610",
  series =       "LNCS",
  pages =        "264--275",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-00971-X",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=264",
  abstract =     "In this paper a method is presented that decreases the
                 necessary number of evaluations in Evolutionary
                 Algorithms. A classifier with confidence information is
                 evolved to replace time consuming evaluations during
                 tournament selection. Experimental analysis of a
                 mathematical example and the application of the method
                 to the problem of evolving walking patterns for
                 quadruped robots show the potential of the presented
                 approach.",
  notes =        "EuroGP'2003 held in conjunction with EvoWorkshops
                 2003",
}

Genetic Programming entries for Jens Ziegler Wolfgang Banzhaf