Bayesian Methods for Efficient Genetic Programming

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

@Article{Zhang:2000:bmeGP,
  author =       "Byoung-Tak Zhang",
  title =        "Bayesian Methods for Efficient Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "3",
  pages =        "217--242",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Bayesian
                 genetic programming, probabilistic evolution, adaptive
                 Occam's razor, incremental data inheritance, parsimony
                 pressure, data subset selection",
  ISSN =         "1389-2576",
  URL =          "http://bi.snu.ac.kr/Publications/Journals/International/GPEM1-3.pdf",
  URL =          "http://citeseer.ist.psu.edu/455254.html",
  DOI =          "doi:10.1023/A:1010010230007",
  size =         "26 pages",
  abstract =     "A Bayesian framework for genetic programming (GP) is
                 presented. This is motivated by the observation that
                 genetic programming iteratively searches populations of
                 fitter programs and thus the information gained in the
                 previous generation can be used in the next generation.
                 The Bayesian GP makes use of Bayes theorem to estimate
                 the posterior distribution of programs from their prior
                 distribution and likelihood for the fitness data
                 observed. Offspring programs are then generated by
                 sampling from the posterior distribution by genetic
                 variation operators. We present two GP algorithms
                 derived from the Bayesian GP framework. One is the
                 genetic programming with the adaptive Occam?s razor
                 (AOR) designed to evolve parsimonious programs. The
                 other is the genetic programming with incremental data
                 inheritance (IDI) designed to accelerate evolution by
                 active selection of fitness cases. A multiagent
                 learning task is used to demonstrate the effectiveness
                 of the presented methods. In a series of experiments,
                 AOR reduced solution complexity by 20% and IDI doubled
                 evolution speed, both without loss of solution
                 accuracy.",
  notes =        "Article ID: 264702",
}

Genetic Programming entries for Byoung-Tak Zhang

Citations