Improving Performance of GP by Adaptive Terminal Selection

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

@InProceedings{ok00improving,
  author =       "Sooyol Ok and Kazuo Miyashita and Seiichi Nishihara",
  title =        "Improving Performance of {GP} by Adaptive Terminal
                 Selection",
  booktitle =    "PRICAI 2000 Topics in Artificial Intelligence: 6th
                 Pacific Rim International Conference on Artificial
                 Intelligence",
  pages =        "435--445",
  year =         "2000",
  editor =       "Riichiro Mizoguchi and John K. Slaney",
  series =       "Lecture Notes in Artifical Intelligence",
  volume =       "1886",
  address =      "Melbourne Convention Centre, Austrlia",
  month =        "28 " # aug # "-1 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67925-1",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  URL =          "http://staff.aist.go.jp/k.miyashita/publications/PRICAI2000.ps",
  URL =          "http://citeseer.ist.psu.edu/394599.html",
  abstract =     "Genetic Programming (GP) is an evolutionary search
                 algorithm which searches a computer program capable of
                 producing the desired solution for a given problem. For
                 the purpose, it is necessary that GP system has access
                 to a set of features that are at least a superset of
                 the features necessary to solve the problem. However,
                 when the feature set given to GP is redundant, GP su
                 ers substantial loss of its eciency. This paper
                 presents a new approach in GP to acquire relevant
                 terminals from a redundant set of terminals. We propose
                 the adaptive mutation based on terminal weighting
                 mechanism for eliminating irrelevant terminals from the
                 redundant terminal set. We show empirically that the
                 proposed method is effective for finding relevant
                 terminals and improving performance of GP in the
                 experiments on symbolic regression problems.",
  notes =        "PRICAI 2000 http://www3.cm.deakin.edu.au/pricai/",
}

Genetic Programming entries for Sooyol Ok Kazuo Miyashita Seiichi Nishihara

Citations