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遗传算法的基本思想是基于达尔文（Ｄａｒｗｉｎ） 进化论和孟德尔（ Ｍｅｎｄｅｌ） 遗传学说，它将问题表示成群体，根据适者生存的原则，从中选择出适应环境的个体进行复制，通过交换、变异两种基本操作产生新一代更适合环境的群体，最后收敛到一个最优个体，求得问题的最优解。遗传算法不是一种单纯的优化算法，而是一种以进化思想为基础的全新的一般方法论，是解决复杂问题的工具。由于这种基于生物进化论的遗传算法具有许多突出的优点，并在人工智能方面表现出很强的鲁棒性，因此，遗传算法作为一种新的优化搜索方法，被广泛地应用于工程中的各种优化问题。
The basic idea of genetic algorithm is based on Darwin’s theory of evolution and Mendel’s theory of inheritance. It represents the problem as a group. According to the principle of the survival of the fittest, individuals who choose to adapt to the environment are selected for replication. Through exchange and mutation, The basic operations produce a new generation of more suitable for the environment groups, and finally converge to an optimal individual, the optimal solution to the problem. Genetic algorithm is not a simple optimization algorithm, but a brand new general methodology based on evolutionary thought and a tool to solve complex problems. Because this kind of biological evolutionary based genetic algorithm has many outstanding advantages and shows strong robustness in artificial intelligence, genetic algorithm is widely used in engineering as a new optimization search method Various optimization problems.