这是一个遗传算法实现多目标优化。对多目标优化,non-domonated整理已用。设计变量可以是二进制,整数,实数的或枚举数据类型。 -This is a GA implementation for multi-objective optimization. For multi-objective optimization, non-domonated sorting has been used. The design variables can be Binary, Integer,

遗传算法优化PID控制，分别采用实数编码和二进制编码-Genetic algorithm to optimize PID control, real-coded and binary coded

二进制编码和实数编码的遗传算法，代码非常详尽，略有难度。-Binary coding and real-coded genetic algorithm, very detailed.

NSGA2解决随机约束问题的自编程序，可以同时实现实数的编码问题和二进制编码问题，采用快速的非支配排序实现全局快速搜索。-NSGA2 random constraint solving problems by ourselves, can simultaneously encode binary encoding issues and problems of real numbers, with fast non-dominated sorting achieve global fast sea

实数编码和二进制编码的遗传算法程序，程序结构清晰，易懂，是学习遗传算法很好的例子。-Real number coding and binary coded genetic algorithm program, the program structure is clear, and easy to understand, is to study the genetic algorithm is a good example.