A review on probabilistic graphical models in evolutionary computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Pedro Larranaga and Hossein Karshenas and 
                 Concha Bielza and Roberto Santana",
  title =        "A review on probabilistic graphical models in
                 evolutionary computation",
  journal =      "Journal of Heuristics",
  year =         "2012",
  volume =       "18",
  number =       "5",
  pages =        "795--819",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, EDA,
                 Probabilistic graphical model, Bayesian network,
                 Evolutionary computation, Estimation of distribution
                 algorithm, model based GP, PIPE, ECGP, GP-EDA",
  ISSN =         "1381-1231",
  language =     "English",
  URL =          "http://dx.doi.org/10.1007/s10732-012-9208-4",
  DOI =          "doi:10.1007/s10732-012-9208-4",
  size =         "25 pages",
  abstract =     "Thanks to their inherent properties, probabilistic
                 graphical models are one of the prime candidates for
                 machine learning and decision making tasks especially
                 in uncertain domains. Their capabilities, like
                 representation, inference and learning, if used
                 effectively, can greatly help to build intelligent
                 systems that are able to act accordingly in different
                 problem domains. Evolutionary algorithms is one such
                 discipline that has employed probabilistic graphical
                 models to improve the search for optimal solutions in
                 complex problems. This paper shows how probabilistic
                 graphical models have been used in evolutionary
                 algorithms to improve their performance in solving
                 complex problems. Specifically, we give a survey of
                 probabilistic model building-based evolutionary
                 algorithms, called estimation of distribution
                 algorithms, and compare different methods for
                 probabilistic modelling in these algorithms.",
  notes =        "Stuff on GP mostly on page 813",

Genetic Programming entries for Pedro Larranaga Hossein Karshenas Concha Bielza Roberto Santana