An improved genetic algorithm with average-bound crossover and wavelet mutation operations

Ling, SH; Leung, FHF

An improved genetic algorithm with average-bound crossover and wavelet mutation operations

Ling, SH

Leung, FHF

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Publication Type:: Journal Article
Citation:: Soft Computing, 2007, 11 (1), pp. 7 - 31
Issue Date:: 2007-01-01

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Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Leung, FHF	en_US
dc.date.issued	2007-01-01	en_US
dc.identifier.citation	Soft Computing, 2007, 11 (1), pp. 7 - 31	en_US
dc.identifier.issn	1432-7643	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14522
dc.description.abstract	This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover and wavelet mutation. By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA. © Springer-Verlag 2006.	en_US
dc.relation.ispartof	Soft Computing	en_US
dc.relation.isbasedon	10.1007/s00500-006-0049-7	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	An improved genetic algorithm with average-bound crossover and wavelet mutation operations	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	11	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	WOS:000239893000002	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover and wavelet mutation. By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA. © Springer-Verlag 2006.

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http://hdl.handle.net/10453/14522