Genetic Programming with Active Data Selection

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

@InProceedings{zhang:1998:GPads,
  author =       "Byoung-Tak Zhang and Dong-Yeon Cho",
  title =        "Genetic Programming with Active Data Selection",
  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 =        "146--153",
  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",
  ISSN =         "0302-9743",
  URL =          "http://bi.snu.ac.kr/Publications/Journals/International/LNAI1585.pdf",
  DOI =          "doi:10.1007/3-540-48873-1_20",
  size =         "8 pages",
  abstract =     "Genetic programming evolves Lisp-like programs rather
                 than fixed size linear strings. This representational
                 power combined with generality makes genetic
                 programming an interesting tool for automatic
                 programming and machine learning. One weakness is the
                 enormous time required for evolving complex programs.
                 In this paper we present a method for accelerating
                 evolution speed of genetic programming by active
                 selection of fitness cases during the run. In contrast
                 to conventional genetic programming in which all the
                 given training data are used repeatedly, the presented
                 method evolves programs using only a subset of given
                 data chosen incrementally at each generation. This
                 method is applied to the evolution of collective
                 behaviors for multiple robotic agents. Experimental
                 evidence supports that evolving programs on an
                 incrementally selected subset of fitness cases can
                 significantly reduce the fitness evaluation time
                 without sacrificing generalization accuracy of the
                 evolved programs.",
  notes =        "SEAL'98 Published as springer-verlag LNAI 1585
                 SEAL98#058

                 A1 Artificial Intelligence Lab (SCAI) Dept. of Computer
                 Engineering Seoul National University Seoul 151-742,
                 Korea {btzhang, dycho}@scai.snu.ac.kr
                 http://scai.snu.ac.kr/",
}

Genetic Programming entries for Byoung-Tak Zhang Dong-Yeon Cho

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