A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study

dc.contributor.author	Herrera Triguero, Francisco
dc.contributor.author	Lozano Márquez, Manuel
dc.date.accessioned	2020-12-17T07:48:32Z
dc.date.available	2020-12-17T07:48:32Z
dc.date.issued	2003
dc.identifier.citation	Herrera, F., Lozano, M., & Sanchez, A. (2003). A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. International Journal of Intelligent Systems, 18(3), 309-338. doi:10.1002/int.10091	es_ES
dc.identifier.uri	http://hdl.handle.net/10481/64963
dc.description.abstract	The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models.	es_ES
dc.language.iso	eng	es_ES
dc.publisher	WILEY	es_ES
dc.rights	Atribución 3.0 España	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/	*
dc.title	A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study	es_ES
dc.type	info:eu-repo/semantics/article	es_ES
dc.rights.accessRights	info:eu-repo/semantics/openAccess	es_ES
dc.identifier.doi	10.1002/int.10091

Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución 3.0 España