@article{MAKHILLRJAS20151039560,
    title = {Combined Crossover Operator},
    journal = {Research Journal of Applied Sciences},
    volume = {10},
    number = {3},
    pages = {75-79},
    year = {2015},
    issn = {1815-932x},
    doi = {rjasci.2015.75.79},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2015.75.79},
    author = {Khalid,Amina,Abdelaziz and},
    keywords = {Evolutionary computation,crossover,genetic algorithms,real coding crossover,CCO},
    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.}
    }