Estimation of Distribution Programming Based on Bayesian Network

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Kohsuke Yanai and Hitoshi Iba",
  title =        "Estimation of Distribution Programming Based on
                 {Bayesian} Network",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "1618--1625",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, Bayesian
                 methods, Benchmark testing, Electronic design
                 automation and methodology, Informatics, Probability
                 distribution, Search methods, Search problems, Tree
                 data structures, Bayes methods, Boolean functions,
                 estimation theory, probability, search problems,
                 Bayesian network, Boolean function, estimation of
                 distribution programming, max problem, population-based
                 program search method, probability distribution,
                 program population",
  ISBN =         "0-7803-7804-0",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2003.1299866",
  abstract =     "In this paper, we propose Estimation of Distribution
                 Programming (EDP) based on a probability distribution
                 expression using a Bayesian network. EDP is a
                 population-based program search method, in which the
                 population probability distribution is estimated, and
                 individuals are generated based on the results. We
                 focus our attention on the fact that the dependency
                 relationship of nodes of the program (expressed as a
                 tree structure) is explicit, and estimate the
                 probability distribution of the program population
                 using a Bayesian network. We compare EDP with GP
                 (Genetic Programming) on several benchmark tests, i.e.,
                 a max problem and a boolean function problem. We also
                 discuss the trends of problems that are the forte of
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",

Genetic Programming entries for Kohsuke Yanai Hitoshi Iba