The Dynamics of Biased Inductive Genetic Programming

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

@InProceedings{nikolaev:1998:dbiGP,
  author =       "Nikolay I. Nikolaev and Vanio Slavov",
  title =        "The Dynamics of Biased Inductive Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and 
                 Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and 
                 David B. Fogel and Max H. Garzon and 
                 David E. Goldberg and Hitoshi Iba and Rick Riolo",
  pages =        "260--268",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  broken =       "http://homepages.gold.ac.uk/nikolaev/gp98.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/26570/http:zSzzSzwww.niss.gov.uazSzCenterzSzarticleszSzpaperszSzeurogp98.pdf/nikolaev98concepts.pdf",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/23849/http:zSzzSzhomepages.gold.ac.ukzSznikolaevzSzgp98.pdf/the-dynamics-of-biased.pdf",
  URL =          "http://citeseer.ist.psu.edu/468314.html",
  size =         "8 pages",
  abstract =     "We propose integrated navigation and fitness biases as
                 efficiency regulators for inductive Genetic
                 Programming. Evolutionary search is easy on smooth
                 landscapes created with size-biased stochastic
                 complexity fitness functions. In order to achieve
                 continuous guidance to unvisited landscape areas, these
                 functions require mutation and crossover applications,
                 biased by the size of the genetic programs. The
                 evolutionary dynamics of this approach is investigated
                 with population diameter, structural entropy and energy
                 estimates. These estimates provide valuable information
                 for the evolutionary algorithm behavior, with which we
                 may explain and predict its search efficiency. We
                 demonstrate empiricaly that the use of integrated
                 biases contributes to achieve efficient performance in
                 learning regular expressions",
  notes =        "GP-98",
}

Genetic Programming entries for Nikolay Nikolaev Vanio Slavov

Citations