Abstract: Selection criteria, crossover and mutation are three major operators involved in the genetic algorithm’s performance. A lot of work has been done on these operators but the crossover has a significant role in the operation of genetic algorithms. A number of crossover operators have been introduced in literature and all have different impact on genetic algorithm’s performance. In this study, we proposed a new crossover operator to improve the performance of genetic algorithms. Proposed operator is applied along with some traditional crossover operators on seven benchmark problems. Simulation studies show a
remarkable performance of the proposed crossover scheme which obtained a fast convergence and better results than existing or traditional crossover operators.
Keywords: Genetic algorithms, crossover operators, benchmark functions, comparison.
How to cite this article:
Abid Hussain, Yousaf Shad Muhammad and Asim Nawaz, Optimization Through Genetic Algorithm with a New and Efficient Crossover Operator, International Journal of Advances in Mathematics, Volume 2018, Number 1, Pages 1-14, 2018.