Genetic Algorithms Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{ryan:2002:EuroGPa,

• title = "Genetic Algorithms Using Grammatical Evolution",
• author = "Conor Ryan and Miguel Nicolau and Michael O'Neill",
• editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
• booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002",
• volume = "2278",
• series = "LNCS",
• pages = "278--287",
• publisher = "Springer-Verlag",
• address = "Kinsale, Ireland",
• publisher_address = "Berlin",
• month = "3-5 " # apr,
• organisation = "EvoNet",
• year = "2002",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, gauge",
• ISBN = "3-540-43378-3",
• DOI = "doi:10.1007/3-540-45984-7_27",
• abstract = "This paper describes the GAUGE system, Genetic Algorithms Using Grammatical Evolution. GAUGE is a position independent Genetic Algorithm that uses Grammatical Evolution with an attribute grammar to dictate what position a gene codes for. GAUGE suffers from neither under-specification nor over-specification, is guaranteed to produce syntactically correct individuals, and does not require any repair after the application of genetic operators. GAUGE is applied to the standard onemax problem, with results showing that its genotype to phenotype mapping and position independence nature do not affect its performance as a normal genetic algorithm. A new problem is also presented, a deceptive version of the Mastermind game, and we show that GAUGE possesses the position independence characteristics it claims, and outperforms several genetic algorithms, including the competent genetic algorithm messy GA.",
• notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",

}

Genetic Programming entries for Conor Ryan Miguel Nicolau Michael O'Neill

Citations