An Analysis of Koza's Computational Effort Statistic for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  title =        "An Analysis of {Koza}'s Computational Effort Statistic
                 for Genetic Programming",
  author =       "Steffen Christensen and Franz Oppacher",
  editor =       "James A. Foster and Evelyne Lutton and 
                 Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "182--191",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  DOI =          "doi:10.1007/3-540-45984-7_18",
  abstract =     "As research into the theory of genetic programming
                 progresses, more effort is being placed on
                 systematically comparing results to give an indication
                 of the effectiveness of sundry modifications to
                 traditional GP. The statistic that is commonly used to
                 report the amount of computational effort to solve a
                 particular problem with 99% probability is Koza's I(M,
                 i, z) statistic. This paper analyzes this measure from
                 a statistical perspective. In particular, Koza's I
                 tends to underestimate the true computational effort,
                 by 25% or more for commonly used GP parameters and run
                 sizes. The magnitude of this underestimate is
                 nonlinearly decreasing with increasing run count,
                 leading to the possibility that published results based
                 on few runs may in fact be unmatchable when replicated
                 at higher resolution. Additional analysis shows that
                 this statistic also under reports the generation at
                 which optimal results are achieved.",
  notes =        "EuroGP'2002, part of \cite{lutton:2002:GP}",

Genetic Programming entries for Steffen Christensen Franz Oppacher