In machine learning applications, Hadoop has been used as a way to scale genetic algorithms for processing large populations of GA individuals (potential solutions).
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)。
Genetic algorithms (GA) together with a boundary sampling strategy are proposed for optimal statistical design.
对于最优统计设计，本文提出了遗传算法(GA)以及边界抽样策略。
By choosing appropriate operators and parameters, genetic algorithms (GA) can solve the traveling salesman problem (TSP) effectively.
通过选择合适的算子和参数，遗传算法(GA)可以有效求解旅行商问题(tsp)。
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