A Bayesian Network Approach to Program Generation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Hasegawa:2008:TEC,
  title =        "A {Bayesian} Network Approach to Program Generation",
  author =       "Yoshihiko Hasegawa and Hitoshi Iba",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2008",
  month =        dec,
  volume =       "12",
  number =       "6",
  pages =        "750--764",
  keywords =     "genetic algorithms, genetic programming, belief
                 networks, probability, trees (mathematics)Bayesian
                 network, conditional probability table, evolutionary
                 algorithms, expanded parse tree, powerful optimization
                 algorithm, probabilistic techniques, program
                 generation",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2008.915999",
  size =         "15 pages",
  abstract =     "Genetic programming (GP) is a powerful optimization
                 algorithm that has been applied to a variety of
                 problems. This algorithm can, however, suffer from
                 problems arising from the fact that a crossover, which
                 is a main genetic operator in GP, randomly selects
                 crossover points, and so building blocks may be
                 destroyed by the action of this operator. In recent
                 years, evolutionary algorithms based on probabilistic
                 techniques have been proposed in order to overcome this
                 problem. In the present study, we propose a new program
                 evolution algorithm employing a Bayesian network for
                 generating new individuals. It employs a special
                 chromosome called the expanded parse tree , which
                 significantly reduces the size of the conditional
                 probability table (CPT). Prior prototype tree-based
                 approaches have been faced with the problem of huge
                 CPTs, which not only require significant memory
                 resources, but also many samples in order to construct
                 the Bayesian network. By applying the present approach
                 to three distinct computational experiments, the
                 effectiveness of this new approach for dealing with
                 deceptive problems is demonstrated.",
  notes =        "POLE, EPT, Kullback-Leibler. Max problem
                 \cite{langdon:1997:MAX}. DMAX deceptive max problem.
                 Royal tree problem.

                 Also known as \cite{4470578}",
}

Genetic Programming entries for Yoshihiko Hasegawa Hitoshi Iba

Citations