Generalized net models of basic genetic algorithm operators

Pencheva, T; Roeva, O; Shannon, A

Generalized net models of basic genetic algorithm operators

Pencheva, T Roeva, O Shannon, A

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Publication Type:: Chapter
Citation:: 2016, 332 pp. 305 - 325
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Pencheva, T	en_US
dc.contributor.author	Roeva, O	en_US
dc.contributor.author	Shannon, A https://orcid.org/0000-0003-0116-0666	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	2016, 332 pp. 305 - 325	en_US
dc.identifier.uri	http://hdl.handle.net/10453/105636
dc.description.abstract	© Springer International Publishing Switzerland 2016. Generalized nets (GN) are applied here to describe some basic operators of genetic algorithms, namely selection, crossover and mutation and different functions for selection (roulette wheel selection method and stochastic universal sampling), different crossover techniques (one-point crossover, two-point crossover, and “cut and splice” technique), as well as mutation operator (mutation operator of the Breeder genetic algorithm). The resulting GN models can be considered as separate modules, but they can also be accumulated into a single GN model to describe a whole genetic algorithm.	en_US
dc.relation.isbasedon	10.1007/978-3-319-26302-1_19	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Generalized net models of basic genetic algorithm operators	en_US
dc.type	Chapter
utslib.citation.volume	332	en_US
utslib.for	0802 Computation Theory and Mathematics	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	332	en_US

Abstract:

© Springer International Publishing Switzerland 2016. Generalized nets (GN) are applied here to describe some basic operators of genetic algorithms, namely selection, crossover and mutation and different functions for selection (roulette wheel selection method and stochastic universal sampling), different crossover techniques (one-point crossover, two-point crossover, and “cut and splice” technique), as well as mutation operator (mutation operator of the Breeder genetic algorithm). The resulting GN models can be considered as separate modules, but they can also be accumulated into a single GN model to describe a whole genetic algorithm.

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http://hdl.handle.net/10453/105636