说明：  TSP (旅行商问题—Traveling Salesman Problem)，是典型的NP完全问题，即其最坏情况下的时间复杂性随着问题规模的增大按指数方式增长，到目前为止不能找到一个多项式时间的有效算法。遗传算法是一种进化算法，其基本原理是仿效生物界中的“物竞天择、适者生存”的演化法则。遗传算法的做法是把问题参数编码为染色体，再利用迭代的方式进行选择、交叉以及变异等运算来交换种群中染色体的信息，最终生成符合优化目标的染色体。实践证明，遗传算法对于解决TSP问题等组合优化问题具有较好的寻优性能。
(TSP (Traveling Salesman Problem) is a typical NP complete Problem, that is, its worst case time complexity increases exponentially with the increase of the Problem size. Genetic algorithm is an evolutionary algorithm, whose basic principle is to imitate the evolutionary law of "natural selection, survival of the fittest" in the biological world. The approach of genetic algorithm is to encode the problem parameters as chromosomes, and then carry out selection, crossover, mutation and other operations in an iterative way to exchange the information of chromosomes in the population, and finally generate chromosomes that meet the optimization goal. It is proved that genetic algorithm has better performance in solving TSP and other combinatorial optimization problems.)