An Approach to Analyze the Performance of Inversion Sequence Crossover and Sequential Constructive Crossover Along With Greedy Mutation Operator for Solving TSP Using Genetic Algorithm

Abstract — Genetic Algorithm (GA) is an optimization method that not only used to find the shortest tour but also, used to find a reasonably short tour quickly, in the case of Travelling Salesman Problem (TSP). In GA, selection, crossovers and mutation are used as a main operator for TSP. The role of crossovers is to generate offspring that are better tours by preserving partial tours from the parents and the main purpose of mutation operator is to make certain change in the chromosome to recover from the stuck condition into local optimal. Here two unconventional crossover (Sequential Constructive Crossover (SCX) and Inverse Sequence Crossover (ISX), Greedy mutation operator and Normal Mutation operators have been used by the conventional selection operator. The best combination among the different combinations of them has been found out and also observed how the diversity of the population changes with the generation for different combinations of operators under consideration. Some instances of asymmetric TSP has considered to analyze the performance of the proposed combinations of GA operators