中文说明：应用背景遗传算法是根据自然界生物染色体进化的数学模型。首先对种群进行初始化，对每个个体计算适应度，生产下一代。如果生成的种群达不到优化的终止条件，则按照适应度选择优良个体、父代进行交叉或变异，生成子代，然后用子代取代父代，再生成下一个子代。循环执行这一过程，直到满足优化终止条件为止。在遗传算法寻优过程中，把训练后的预测结果作为个体适应度值，通过选择、交叉、变异寻找非线性系统的最优值，分别用函数写出关键技术该算法和思路可以运用到具有非线性系统的寻优问题上，在解决类似黑箱问题也有很好的帮助。而且遗传算法对目标函数的限制极少，不要求连续和可微，可以对一系列离散点进行寻优，所以遗传算法有很好的推广价值。
English Description：
Application backgroundGenetic algorithm is based on the mathematical model of the evolution of the nature of the organism. Firstly, the population is initialized, and the adaptation degree is calculated for each individual. If the generated population can not reach the optimal conditions, the generation of offspring will be replaced by the adaptive selection of the individuals and the parents. Loop to execute the process until it is satisfied that the termination conditions are optimized. In the process of genetic algorithm optimization, the prediction results of the training are used as the fitness value of individual, and the optimal value of the nonlinear system is found by choosing, crossing and mutation.Key TechnologyThe algorithms and ideas can be applied to a nonlinear optimization problem, in solving the similar black box problems also have very good help. And the genetic algorithm has few restrictions on the objective function, and it