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。
The algorithm used natural number coding method with dynamically adjustment for the probability coefficients of crossover and mutation, and used chaos optimization method as the mut.
对于模型的求解方法，构造了一种自适应的混沌遗传算法，采用自然数编码方式，动态的在线调整算法的交叉和变异概率，并采用混沌优化方法作为变异算子。
The methods we proposed include adding deterministic simplex searching operation, improving the crossover operator, modifying adaptive crossover probability and adaptive mutation probability.
采用的相关技术包括增加单纯形寻优算子、运用改进的交叉算子、自适应地调整交叉率和变异率等。
In the process of evolution, the adaptive crossover probability and mutation probability are developed.
在进化过程中采用自适应交叉概率和变异概率。
The natural number coding, the random population selection, the simple crossover strategy of "two-parents-and-one-kid" and the fixed mutation probability were totally adopted.
在算法中采用了自然数编码、随机选取种群、简单的“双亲单子”交叉策略和固定的突变概率。
The natural number coding, the random population selection, the simple crossover strategy of "two-parents-and-one-kid" and the fixed mutation probability were totally adopted.
在算法中采用了自然数编码、随机选取种群、简单的“双亲单子”交叉策略和固定的突变概率。
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