TSP问题是一个典型的、容易描述但是难以处理的NP完全问题，同时TSP问题也是诸多领域内出现的多种复杂问题的集中概括和简化形式。目前求解TSP问题的主要方法有启发式搜索法、模拟退火算法、遗传算法、Hopfield神经网络算法、二叉树描述算法。所以，有效解决TSP问题在计算理论上和实际应用上都有很高的价值，而且TSP问题由于其典型性已经成为各种启发式的搜索、优化算法的间接比较标准(如遗传算法、神经网络优化、列表寻优(TABU)法、模拟退火法等)。遗传算法就其本质来说，主要是解决复杂问题的一种鲁棒性强的启发式随机搜索算法。因此遗传算法在TSP问题求解方面的应用研究，对于构造合适的遗传算法框架、建立有效的遗传操作以及有效地解决TSP问题等有着多方面的重要意义。-The TSP The problem is a typical, easy to describe but difficult to handle the NP-complete problem, the TSP many areas centralized summarized and simplified form of a variety of complex issues. The main method of solving TSP heuristic search method, simulated annealing, genetic algorithm, Hopfield neural network algorithm, the binary tree to describe the algorithm. Therefore, an effective solution to the TSP has a very high value in the calculation of the theoretical and practical applications, and TSP problem has become due to its typical variety of heuristic search, optimization of indirect comparison standard (such as genetic algorithms, neural networks optimization list optimization (TABU), simulated annealing, etc.). The genetic algorithm is by its very nature, a robustness to solve complex problems heuristic random search algorithm. Genetic algorithm TSP problem solving aspects of applied research, genetic algorithm framework for constructing a suitable, effective genetic manipul