Estimation of Bayesian network for program generation

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

@InProceedings{Hasegawa:2006:ASPGP,
  title =        "Estimation of {Bayesian} network for program
                 generation",
  author =       "Yoshihiko Hasegawa and Hitoshi Iba",
  booktitle =    "Proceedings of the Third Asian-Pacific workshop on
                 Genetic Programming",
  year =         "2006",
  editor =       "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen",
  pages =        "35--46",
  ISSN =         "18590209",
  address =      "Military Technical Academy, Hanoi, VietNam",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.iba.k.u-tokyo.ac.jp/~hasegawa/hasegawa_aspgp2006.pdf",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/hasegawa.pdf",
  size =         "12 pages",
  abstract =     "Genetic Programming (GP) is a powerful optimisation
                 algorithm, which employs crossover for a main genetic
                 operator. Because a crossover operator in GP selects
                 sub-trees randomly, the building blocks may be
                 destroyed by crossover. Recently, algorithms called
                 PMBGPs (Probabilistic Model Building GP) based on
                 probabilistic techniques have been proposed in order to
                 improve the problem above. We propose a new PMBGP
                 employing Bayesian network for generating new
                 individuals with a special chromosome called expanded
                 parse tree, which much reduces the number of possible
                 symbols at each node. Although the large number of
                 symbols gives rise to the large conditional probability
                 table and requires a lot of samples to estimate the
                 interactions among nodes, a use of the expanded parse
                 tree overcomes these problems. A computational
                 experiment on a deceptive MAX problem (DMAX problem)
                 demonstrates that our new PMBGP is superior to other
                 program evolution methods.",
  notes =        "http://www.aspgp.org",
}

Genetic Programming entries for Yoshihiko Hasegawa Hitoshi Iba

Citations