Learning Probabilistic Tree Grammars for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Bosman:PPSN:2004,

• author = "Peter A. N. Bosman and Edwin D. {de Jong}",
• title = "Learning Probabilistic Tree Grammars for Genetic Programming",
• booktitle = "Parallel Problem Solving from Nature - PPSN VIII",
• year = "2004",
• editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel",
• volume = "3242",
• pages = "192--201",
• series = "LNCS",
• address = "Birmingham, UK",
• publisher_address = "Berlin",
• month = "18-22 " # sep,
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, EDA",
• ISBN = "3-540-23092-0",
• URL = "http://www.cs.uu.nl/~dejong/publications/edagpppsn.pdf",
• URL = "https://rdcu.be/dc0ju",
• DOI = "doi:10.1007/b100601",
• DOI = "doi:10.1007/978-3-540-30217-9_20",
• size = "10 pages",
• abstract = "Genetic Programming (GP) provides evolutionary methods for problems with tree representations. A recent development in Genetic Algorithms (GAs) has led to principled algorithms called Estimation-of-Distribution Algorithms (EDAs). EDAs identify and exploit structural features of a problems structure during optimization. Here, we investigate the use of a specific EDA for GP. We develop a probabilistic model that employs transformations of production rules in a context-free grammar to represent local structures. The results of performing experiments on two benchmark problems demonstrate the feasibility of the approach.",
• notes = "'The results indicate that our EDA for GP is feasible.'",

}

Genetic Programming entries for Peter A N Bosman Edwin D de Jong

Citations