Latent Variable Model for Estimation of Distribution Algorithm Based on a Probabilistic Context-Free Grammar

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  title =        "Latent Variable Model for Estimation of Distribution
                 Algorithm Based on a Probabilistic Context-Free
  author =       "Yoshihiko Hasegawa and Hitoshi Iba",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2009",
  month =        aug,
  volume =       "13",
  number =       "4",
  pages =        "858--878",
  keywords =     "genetic algorithms, genetic programming, EM algorithm,
                 estimation of distribution algorithm, variational
                 Bayes.context-sensitive grammars, probability context
                 freedom assumption, distribution algorithm estimation,
                 evolutionary algorithm, function evolution, genetic
                 operator, genetic programming techniques, latent
                 variable model, probabilistic context-free grammar,
                 probabilistic program evolution, probabilistic
  DOI =          "doi:10.1109/TEVC.2009.2015574",
  ISSN =         "1089-778X",
  size =         "21 pages",
  abstract =     "Estimation of distribution algorithms are evolutionary
                 algorithms using probabilistic techniques instead of
                 traditional genetic operators. Recently, the
                 application of probabilistic techniques to program and
                 function evolution has received increasing attention,
                 and this approach promises to provide a strong
                 alternative to the traditional genetic programming
                 techniques. Although a probabilistic context-free
                 grammar (PCFG) is a widely used model for probabilistic
                 program evolution, a conventional PCFG is not suitable
                 for estimating interactions among nodes because of the
                 context freedom assumption. In this paper, we have
                 proposed a new evolutionary algorithm named programming
                 with annotated grammar estimation based on a PCFG with
                 latent annotations, which allows this context freedom
                 assumption to be weakened. By applying the proposed
                 algorithm to several computational problems, it is
                 demonstrated that our approach is markedly more
                 effective at estimating building blocks than prior
  notes =        "PAGE. Royal tree, DMAX complex arithmetic Also known
                 as \cite{5175364}",

Genetic Programming entries for Yoshihiko Hasegawa Hitoshi Iba