Grammar-Based Genetic Programming with Bayesian Network

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

@InProceedings{Wong:2014:CEC,

• title = "Grammar-Based Genetic Programming with {Bayesian} Network",
• author = "Pak-Kan Wong and Leung-Yau Lo and Man-Leung Wong and Kwong-Sak Leung",
• pages = "739--746",
• booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation",
• year = "2014",
• month = "6-11 " # jul,
• editor = "Carlos A. {Coello Coello}",
• address = "Beijing, China",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Evolutionary Algorithms with Statistical and Machine Learning Techniques, Estimation of distribution algorithms",
• DOI = "doi:10.1109/CEC.2014.6900423",
• abstract = "Grammar-Based Genetic Programming (GBGP) improves the search performance of Genetic Programming (GP) by formalising constraints and domain specific knowledge in grammar. The building blocks (i.e. the functions and the terminals) in a program can be dependent. Random crossover and mutation destroy the dependence with a high probability, hence breeding a poor program from good programs. Understanding on the syntactic and semantic in the grammar plays an important role to boost the efficiency of GP by reducing the number of poor breeding. Therefore, approaches have been proposed by introducing context sensitive ingredients encoded in probabilistic models. In this paper, we propose Grammar-Based Genetic Programming with Bayesian Network (BGBGP) which learns the dependence by attaching a Bayesian network to each derivation rule and demonstrates its effectiveness in two benchmark problems.",
• notes = "WCCI2014",

}

Genetic Programming entries for Pak-Kan Wong "Peter" Leung-Yau Lo Man Leung Wong Kwong-Sak Leung

Citations