Estimation of Bayesian network for program generation

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

@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://gpbib.cs.ucl.ac.uk/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 = "broken march 2020 http://www.aspgp.org",

}

Genetic Programming entries for Yoshihiko Hasegawa Hitoshi Iba

Citations