Genetic algorithm for Traveling Salesman Problem

Abstract

Traveling Salesman Problem (TSP) is one of the most famous NP-hard problems which is hard to find an optimal solution. Many heuristic algorithms are applied to find a suboptimal solution in a limited time. In this paper, we employ a Genetic Algorithm (GA) to solve the TSP, and a further study is conducted by evaluating the performance of different crossover and mutation methods with a heuristic strategy. Four experiments with different parameters are designed, which apply instances from benchmark TSPLIB. Partial-mapped crossover and rotate mutation with offspring-parent competition strategy has shown efficient gets the best results.