Progress Rate in Noisy Genetic Programming for Choosing lambda

Created by W.Langdon from gp-bibliography.bib Revision:1.4031

  author =       "Jean-Baptiste Hoock and Olivier Teytaud",
  title =        "Progress Rate in Noisy Genetic Programming for
                 Choosing lambda",
  booktitle =    "Artificial Evolution",
  year =         "2011",
  editor =       "Jin-Kao Hao and Pierrick Legrand and Pierre Collet and 
                 Nicolas Monmarch and Evelyne Lutton and 
                 Marc Schoenauer",
  pages =        "494--505",
  address =      "Angers, France",
  month =        "24-26 " # oct,
  organisation = "Association Evolution Artificielle",
  keywords =     "genetic algorithms, genetic programming, game theory",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "12 pages",
  abstract =     "Recently, it has been proposed to use Bernstein races
                 for implementing non-regression testing in noisy
                 genetic programming. We study the population size of
                 such a (1+lambda) evolutionary algorithm applied to a
                 noisy fitness function optimisation by a progress rate
                 analysis and experiment it on a policy search
  notes =        "

                 See also Chapter 4 in \cite{Hoock:thesis}

                 1 TAO (Inria), LRI, UMR 8623(CNRS - Univ. Paris-Sud),
                 bat 490 Univ. Paris-Sud 91405 Orsay, France, 2 Dept. of
                 Computer Science and Information Engineering, National
                 University of Tainan, Taiwan EA'11",
  language =     "ENG",
  oai =          "",

Genetic Programming entries for Jean-Baptiste Hoock Olivier Teytaud