A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study Herrera Triguero, Francisco Lozano Márquez, Manuel 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. 2020-12-17T07:48:32Z 2020-12-17T07:48:32Z 2003 info:eu-repo/semantics/article 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 http://hdl.handle.net/10481/64963 10.1002/int.10091 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España WILEY