Estimation of Distribution Programming Based on Bayesian Network

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Yanai:2003:EodpboBn,

• author = "Kohsuke Yanai and Hitoshi Iba",
• title = "Estimation of Distribution Programming Based on {Bayesian} Network",
• booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
• editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
• pages = "1618--1625",
• year = "2003",
• publisher = "IEEE Press",
• address = "Canberra",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "8-12 " # dec,
• organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• keywords = "genetic algorithms, genetic programming, Bayesian methods, Benchmark testing, Electronic design automation and methodology, Informatics, Probability distribution, Search methods, Search problems, Tree data structures, Bayes methods, Boolean functions, estimation theory, probability, search problems, Bayesian network, Boolean function, estimation of distribution programming, max problem, population-based program search method, probability distribution, program population",
• ISBN = "0-7803-7804-0",
• URL = "http://www.iba.k.u-tokyo.ac.jp/papers/2003/yanaiCEC2003.pdf",
• DOI = "doi:10.1109/CEC.2003.1299866",
• abstract = "In this paper, we propose Estimation of Distribution Programming (EDP) based on a probability distribution expression using a Bayesian network. EDP is a population-based program search method, in which the population probability distribution is estimated, and individuals are generated based on the results. We focus our attention on the fact that the dependency relationship of nodes of the program (expressed as a tree structure) is explicit, and estimate the probability distribution of the program population using a Bayesian network. We compare EDP with GP (Genetic Programming) on several benchmark tests, i.e., a max problem and a boolean function problem. We also discuss the trends of problems that are the forte of EDP.",
• notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.",

}

Genetic Programming entries for Kohsuke Yanai Hitoshi Iba

Citations