Research Journal of Applied Sciences
ISSN: Online 1993-6079ISSN: Print 1815-932x
Abstract
Genetic algorithms are optimization and search methods based on the principles of Darwinian evolution and genetics that try to provide the optimal solution of a problem. They evolve a population of candidate solutions to the problem, using mutation, crossover and selection operators. Based on the diversity and the efficiency of four well known crossover operators, this study presents a novel operator called Combined Crossover Operator (CCO). The comparison with those four crossover operators shows that the results obtained by the CCO are promising.
How to cite this article:
Khalid Jebari, Amina Dik, Abdelaziz Bouroumi and Aziz Ettouhami. Combined Crossover Operator.
DOI: https://doi.org/10.36478/rjasci.2015.75.79
URL: https://www.makhillpublications.co/view-article/1815-932x/rjasci.2015.75.79