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This paper proposes a new solution for Traveling Salesman Problem (TSP), using genetic algorithm. A heuristic crossover and mutation operation have been proposed to prevent premature convergence. Presented operations try not only to solve this challenge by means of a heuristic function but also considerably accelerate the speed of convergence by reducing excessively the number of generations. By considering TSP's evaluation function, as a traveled route among all n cities, the probability of crossover and mutation have been adaptively and nonlinearly tuned. Experimental results demonstrate that proposed algorithm due to the heuristic performance is not easily getting stuck in local optima and has a reasonable convergent speed to reach the global optimal solution. Besides, implementation of the algorithm does not have any complexities.