Performance analysis of a novel crossover technique on permutation encoded genetic algorithms

The Performance of GA is mainly dependent on two factors are chromosome representation and the selection of relevant genetic operators such as selection, crossover and mutation. Many GA crossover operators have been invented by researchers because the performance of GA depends on an ability of these operators. Though there are several crossover techniques available, these are randomly removes the duplicate genes in a chromosome lead to more computation time to converge with optimal solution. Since most of them do not have stable model. Removing duplicate genes in a chromosome is a hectic process in GA. To overcome these difficulties, this paper uses a novel crossover called Fast Order Mapped Crossover (FOMX) which does not perform randomness and gene level comparison to find duplicate genes in individuals. To prove this technique, travelling salesperson problem (tsp) has chosen in order to find the optimal path of a tour. This technique is applied on different tsp instances and the obtained results are compared with the existing crossover techniques.