遗传算法的程序

遗传 算 法 (GeneticA lgorithm,G A)是一种大规模并行搜索优化算法，它模

拟了达尔文“适者生存”的进化规律和随机信息交换思想，仿效生物的遗传方式，

从随机生成的初始解群出发，开始搜索过程。解群中的个体称为染色体，它是一

串符号，可以是一个二进制字符串，也可以是十进制字符串或采用其他编码方式

形成的码串。对父代(当前代)群体进行交叉、变异等遗传操作后，根据个体的

适应度〔fitness)进行选择操作，适应度高的个体有较高的概率被选中并复制到下

一代，如此产生的子代通常优于父代，这个过程称为进化。上述过程循环执行直

至满足停机条件，最终使优化过程以大概率趋于全局最优解-procedures GA GA (GeneticA lgorithm. A G) is a massively parallel search algorithm, It simulated by Darwin's "survival of the fittest" law of evolution and random information exchange ideas, to follow the example of biological genetic, randomly generated from the initial solutions group, began searching process. Groups of individuals known as chromosome, which is a bunch of symbols, it is a binary string, it can also be a decimal string or other code created by the code string. On behalf of the father (the current generation) groups for the crossover and mutation operation. According to the individual fitness [fitness), select Options, Adaptation to high-level individuals have a higher probability of being selected and copied to the next generation, so the offspring are usual