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A taxonomy for the crossover operator for real‐coded genetic algorithms: An experimental study
Author(s) -
Herrera F.,
Lozano M.,
Sánchez A. M.
Publication year - 2003
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10091
Subject(s) - crossover , operator (biology) , computer science , taxonomy (biology) , genetic algorithm , algorithm , artificial intelligence , theoretical computer science , machine learning , gene , biology , genetics , botany , repressor , transcription factor
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. © 2003 Wiley Periodicals, Inc.