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

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@Article{Hasegawa:2009:ieeeTEC,
  title =        "Latent Variable Model for Estimation of Distribution
                 Algorithm Based on a Probabilistic Context-Free
                 Grammar",
  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
                 techniques",
  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
  approaches.},
  notes =	{PAGE.
Royal tree, DMAX complex arithmetic
Also known as \cite{5175364}},
)

Genetic Programming entries for Toshihiko Yanase Hitoshi Iba