The core idea of the algorithm is to use the cloud model theory to generate crossover and mutation probability.
算法的核心思想在于利用云模型理论生成交叉和变异概率。
The crossover probability and mutation probability are improved on self-proper theory, so that the proper numbers of crossover probability and mutation probability can be found.
运用自适应理论改进交叉概率及变异概率，算法本身总能找到适合于自己得交叉概率和变异概率。
We proved the convergence of GA which we proposed adaptive fitness functions, adaptive crossover probability and adaptive mutation probability.
最后，对提出的自适应交叉概率和自适应变异概率利用遗传算法的公理化理论进行了收敛性的证明。
应用推荐