Efficient Crossover in the GAuGE System

Miguel Nicolau; Conor Ryan

doi:10.1007/978-3-540-24650-3_12

Efficient Crossover in the GAuGE System

Miguel Nicolau, Conor Ryan

Department of Computer Science & Information Systems

University of Limerick

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

Abstract

This paper presents a series of context-preserving crossover operators for the GAuGE system. These operators have been designed to respect the representation of genotype strings in GAuGE, thereby making sensible changes at the genotypic level. Results on a set of problems suggest that some of these operators can improve the maintenance and propagation of building blocks in GAuGE, as well as its scalability, and could be of use to other systems using structural evolving genomes.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Maarten Keijzer, Simon M. Lucas, Ernesto Costa, Terence Soule, Una-May O’Reilly
Publisher	Springer Verlag
Pages	125-137
Number of pages	13
ISBN (Print)	3540213465, 9783540213468
DOIs	https://doi.org/10.1007/978-3-540-24650-3_12
Publication status	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3003
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-24650-3_12

Cite this

Nicolau, M., & Ryan, C. (2004). Efficient Crossover in the GAuGE System. In M. Keijzer, S. M. Lucas, E. Costa, T. Soule, & U.-M. O’Reilly (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 125-137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3003). Springer Verlag. https://doi.org/10.1007/978-3-540-24650-3_12

Nicolau, Miguel ; Ryan, Conor. / Efficient Crossover in the GAuGE System. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Maarten Keijzer ; Simon M. Lucas ; Ernesto Costa ; Terence Soule ; Una-May O’Reilly. Springer Verlag, 2004. pp. 125-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Nicolau, M & Ryan, C 2004, Efficient Crossover in the GAuGE System. in M Keijzer, SM Lucas, E Costa, T Soule & U-M O’Reilly (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3003, Springer Verlag, pp. 125-137. https://doi.org/10.1007/978-3-540-24650-3_12

Efficient Crossover in the GAuGE System. / Nicolau, Miguel; Ryan, Conor.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Maarten Keijzer; Simon M. Lucas; Ernesto Costa; Terence Soule; Una-May O’Reilly. Springer Verlag, 2004. p. 125-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3003).

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

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Nicolau M, Ryan C. Efficient Crossover in the GAuGE System. In Keijzer M, Lucas SM, Costa E, Soule T, O’Reilly UM, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 125-137. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24650-3_12

Efficient Crossover in the GAuGE System

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