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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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 Yoshihiko Hasegawa Hitoshi Iba

Citations