Impact of genetic operators on the performance of genetic algorithm (GA) for travelling salesman problem (TSP) Available to Purchase

This paper is focused on travelling salesman problem (TSP) and the impact of genetic operators such as crossover and mutation of Genetic Algorithm (GA). GA is a heuristic technique and is inspired by biological changes. Its performance is compared on different coding platform for different values of different genetic operators. The GA incorporates a few parameters that ought to be balanced, to get dependable outcomes. This paper proposes that for different values of genetic operators of GAs, the optimum value of outcome will be modified appropriately because the GA can adapt its operator’s values for a specific problem quickly. Populace evolution or development emerges by using these different genetic operators iteratively and gives a correct solution or a solution with minimum error. This paper provides a study of the impact of different operators on the performance of GA for the optimized solution of TSP. All experiments conducted on python, C and Ruby for the solution of TSP and significant plots generated are useful for researchers.