说明：  遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法 遗传算法的基本运算过程如下： a)初始化：设置进化代数计数器t=0，设置最大进化代数T，随机生成M个个体作为初始群体P(0)。 b)个体评价：计算群体P(t)中各个个体的适应度。 c)选择运算:将选择算子作用于群体。选择的目的是把优化的个体直接遗传到下一代或通过配对交叉产生新的个体再遗传到下一代。选择操作是建立在群体中个体的适应度评估基础上的。 d)交叉运算：将交叉算子作用于群体。所谓交叉是指把两个父代个体的部分结构加以替换重组而生成新个体的操作。遗传算法中起核心作用的就是交叉算子。
(Genetic algorithm (Genetic Algorithm) is a computational model of biological evolution of natural selection and genetic mechanism of biological evolution of the simulation of Darwin, is a kind of method to search the optimal solution by simulating natural evolutionary process The basic operation process of genetic algorithm as follows: A initialization settings): the evolution algebra counter t=0, set the maximum evolution algebra T, randomly generated M individuals as the initial population of P (0). B) individual uation: Calculation of group P (T) in the fitness of each individual. C) selecting operation: the selection operator acting on the group. The choice of the purpose is to direct individual genetic optimization to the next generation, or by paired crossover generates new individuals and then transmitted to the next generation. The choice of operation is based on individual fitness uation based on. D) crossover: crossover operator acting on the group. The so-called cross)