在计算机学习用户程序中，Hadoop已经作为处理大量GA个体的规模遗传算法的一种方法(潜在解决方案)。
In machine learning applications, Hadoop has been used as a way to scale genetic algorithms for processing large populations of GA individuals (potential solutions).
文中首先对模糊控制和遗传算法的基本原理进行了介绍和探讨，内容主要包括：模糊控制的数学基础、常规模糊控制器的设计、遗传算法的基本操作以及模式理论等。
Firstly, fuzzy control and GA is introduced and discussed, such as the Mach base of fuzzy control, the design of conventional fuzzy controller, the basic operation of GA and schemata theory and so on.
近年来随着工程领域中复杂的大规模非线性系统的出现，遗传算法日益得到青睐，目前已经广泛应用到各个领域中。
With the emerge of complex large scale nonlinear system, GA has been used increasingly in recent years and now GA can be found in many fields.
通过分支定界法对小规模算例的验证表明，本遗传算法获得精确解的比例是高的，由此认为所给遗传算法是很有效的。
The proposed GA algorithm is verified effective by comparing with the Branch and Bound method on small sized numerical experiments.
我们的实验结果表明使用离散逻辑斯蒂模型来控制种群规模的VPGA能够比其他从微观算子上改进的遗传算法更加高效省时。
Experimental results show that VPGA using logistic model population size is more efficient and requires less computation time than other modified GAs which only improve the GA operations.
该方法较原来的随机选择过程简单，且减少了遗传算法的种群规模。
This method is more simple than original stochastic choice process , also reduces the GA population scale.
实践证明，遗传算法作为现代最优化的手段，它应用于大规模、复杂空间领域离散值情况下的全局最优化问题是合适的。
The practice proved that GA is an optimized method of the modern. It is appropriate to be used in large-scale and complex discrete space GlobalOptimization.
该算法对传统遗传算法的编码方式、群体规模以及遗传算子等方面进行了改进，利用专家知识辅助搜寻可行解，并提出罚因子的自适应调整。
This algorithm improves the method of coding, size of population and operators; USES expert knowledge to aid searching feasible solution; and presents self adaptive adjusting of penalty factor.
该算法对传统遗传算法的编码方式、群体规模以及遗传算子等方面进行了改进，利用专家知识辅助搜寻可行解，并提出罚因子的自适应调整。
This algorithm improves the method of coding, size of population and operators; USES expert knowledge to aid searching feasible solution; and presents self adaptive adjusting of penalty factor.
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