Genetic algorithms using grammatical evolution

Conor Ryan; Miguel Nicolau; Michael O’Neill

doi:10.1007/3-540-45984-7_27

Genetic algorithms using grammatical evolution

Conor Ryan, Miguel Nicolau, Michael O’Neill

Department of Computer Science & Information Systems

University of Limerick

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 messyGA.

Original language	English
Title of host publication	Genetic Programming - 5th European Conference, EuroGP 2002, Proceedings
Editors	James A. Foster, Evelyne Lutton, Julian Miller, Conor Ryan, Andrea G.B. Tettamanzi
Publisher	Springer Verlag
Pages	278-287
Number of pages	10
ISBN (Print)	9783540433781
DOIs	https://doi.org/10.1007/3-540-45984-7_27
Publication status	Published - 2002
Event	5th European Conference on Genetic Programming, EuroGP 2002 - Kinsale, Ireland Duration: 3 Apr 2002 → 5 Apr 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2278
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th European Conference on Genetic Programming, EuroGP 2002
Country/Territory	Ireland
City	Kinsale
Period	3/04/02 → 5/04/02

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10.1007/3-540-45984-7_27

Cite this

Ryan, C., Nicolau, M., & O’Neill, M. (2002). Genetic algorithms using grammatical evolution. In J. A. Foster, E. Lutton, J. Miller, C. Ryan, & A. G. B. Tettamanzi (Eds.), Genetic Programming - 5th European Conference, EuroGP 2002, Proceedings (pp. 278-287). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2278). Springer Verlag. https://doi.org/10.1007/3-540-45984-7_27

Ryan, Conor ; Nicolau, Miguel ; O’Neill, Michael. / Genetic algorithms using grammatical evolution. Genetic Programming - 5th European Conference, EuroGP 2002, Proceedings. editor / James A. Foster ; Evelyne Lutton ; Julian Miller ; Conor Ryan ; Andrea G.B. Tettamanzi. Springer Verlag, 2002. pp. 278-287 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Genetic algorithms using grammatical evolution",

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 messyGA.",

author = "Conor Ryan and Miguel Nicolau and Michael O{\textquoteright}Neill",

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Ryan, C, Nicolau, M & O’Neill, M 2002, Genetic algorithms using grammatical evolution. in JA Foster, E Lutton, J Miller, C Ryan & AGB Tettamanzi (eds), Genetic Programming - 5th European Conference, EuroGP 2002, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2278, Springer Verlag, pp. 278-287, 5th European Conference on Genetic Programming, EuroGP 2002, Kinsale, Ireland, 3/04/02. https://doi.org/10.1007/3-540-45984-7_27

Genetic algorithms using grammatical evolution. / Ryan, Conor; Nicolau, Miguel; O’Neill, Michael.
Genetic Programming - 5th European Conference, EuroGP 2002, Proceedings. ed. / James A. Foster; Evelyne Lutton; Julian Miller; Conor Ryan; Andrea G.B. Tettamanzi. Springer Verlag, 2002. p. 278-287 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2278).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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 messyGA.

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Ryan C, Nicolau M, O’Neill M. Genetic algorithms using grammatical evolution. In Foster JA, Lutton E, Miller J, Ryan C, Tettamanzi AGB, editors, Genetic Programming - 5th European Conference, EuroGP 2002, Proceedings. Springer Verlag. 2002. p. 278-287. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45984-7_27

Genetic algorithms using grammatical evolution

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