本文详述遗传算法的起源、实现、及应用和存在的问题。遗传算法是仿自然界的自然选择法则设计的。算法源于一群随机基因组，通过一定的适应性判决消除适应性低的基因组，保留适应性中等的和高的基因组；并在高适应性的基因组中，随机进行变异和组配，将基因组补足到恒定的数量，再进行适应性判决，一直到满足问题的要求。本文就此法做了中国旅行商题，实验效果非常满意，产生的结果比用Ｈｏｐｆｉｅｌｄ神经网络计算结果要好得多。 This article details the origin, realization, application and existing problems of genetic algorithms. Genetic algorithm is the natural selection of natural law design rules. The algorithm stems from a group of randomized genomes that eliminate poorly adapted genomes by some adaptive decision and retain moderate and high-adaptable genomes. Mutations and recombination are performed randomly in highly-adapted genomes to complement the genomes A constant amount, then adaptive judgments, to meet the requirements of the problem. In this paper, we do a Chinese travel business problem, the experimental results are very satisfactory, the results produced by using Hopfield neural network results are much better.