  The genetic algorithm includes some parameters that should be adjusted, so as
to get reliable results. Choosing a representation of the problem addressed, an
initial population, a method of selection, a crossover operator, mutation
operator, the probabilities of crossover and mutation, and the insertion method
creates a variant of genetic algorithms. Our work is part of the answer to this
perspective to find a solution for this combinatorial problem. What are the
best parameters to select for a genetic algorithm that creates a variety
efficient to solve the Travelling Salesman Problem (TSP)? In this paper, we
present a comparative analysis of different mutation operators, surrounded by a
dilated discussion that justifying the relevance of genetic operators chosen to
solving the TSP problem.
