[Hameed et. al., Vol.5 (Iss.2): February, 2017] ISSN- 2350-0530(O), ISSN- 2394-3629(P) ICV (Index Copernicus Value) 2015: 71.21 IF: 4.321 (CosmosImpactFactor), 2.532 (I2OR) InfoBase Index IBI Factor 3.86 Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [284] Science A COMPARATIVE STUDY OF CROSSOVER OPERATORS FOR GENETIC ALGORITHMS TO SOLVE TRAVELLING SALESMAN PROBLEM Wafaa Mustafa Hameed *1 , Asan Baker Kanbar 2 *1, 2 Assistant Lecturer, Department of Computer Science, Cihan University \ Campus \ Sulaimaniya, Iraq DOI: https://doi.org/10.5281/zenodo.345734 Abstract Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used , one of the problems in using genetic algorithms is the choice of crossover operator Many crossover operators have been proposed in literature on evolutionary algorithms, however, it is still unclear which crossover operator works best for a given optimization problem. This paper aims at studying the behavior of different types of crossover operators in the performance of genetic algorithm. These types of crossover are implemented on Traveling Salesman Problem (TSP); Whitley used the order crossover (OX) depending on specific parameters to solve the traveling salesman problem, the aim of this paper is to make a comparative study between order crossover (OX) and other types of crossover using the same parameters which was Whitley used. Keywords: Genetic Algorithms; Crossover Operator; Traveling Salesman Problem; Order Crossover; Parameters, Natural Evaluation, Evolutionary Algorithms. Cite This Article: Wafaa Mustafa Hameed, and Asan Baker Kanbar. (2017). “A COMPARATIVE STUDY OF CROSSOVER OPERATORS FOR GENETIC ALGORITHMS TO SOLVE TRAVELLING SALESMAN PROBLEM.” International Journal of Research - Granthaalayah, 5(2), 284-291. https://doi.org/10.5281/zenodo.345734. 1. Introduction Genetic algorithms (GAs) are parts of the evolutionary computing, which is a rapidly growing area of artificial intelligence. (GAs) are inspired by Darwin's theory about evolution. Simply said, solution to a problem solved by genetic algorithms is evolved. Genetic Algorithms (GAs) were first suggested by John Holland and developed by him and his students and colleagues in the seventies. This leads to Holland's book “Adoption in Natural and Artificial Systems''