Therapeutic solutions that target the behavioral, chemical, and genetic components of the disease are all in development.
针对这一疾病行为性的，化学物质的和基因因素的治疗方法仍在研究当中。
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)。
One advantage of multi-objective genetic optimization algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run.
多目标遗传优化算法的一个优点就是可在一次迭代计算中寻找到问题的多个非劣最优解。
There are a number of unfeasible scheduling solutions in the Job-shop Scheduling Problem (JSP), it seriously affects the quality of Genetic Algorithms(GA) searching for the best solution.
在作业车间调度问题中，存在大量的不可行调度解，严重影响了遗传算法查找 最 优调度的质量。
There are a number of unfeasible scheduling solutions in the Job-shop scheduling Problem (JSP), it seriously affects the quality of Genetic Algorithms (GA) searching for the best solution.
在作业车间调度问题中，存在大量的不可行调度解，严重影响了遗传算法查找最优调度的质量。
For improving the quality of the solutions and the calculation efficiency, the paper improved genetic algorithms, and applied to solve the reactive power optimization in power system.
为提高解的质量与计算效率，对遗传算法做了改进，并将其应用于电力系统无功优化中。
The conflict, vacancy and unfeasible solutions may appear when the traditional genetic algorithms are applied into the optimizing allocation.
在把传统的遗传算法应用于优化分配问题时，会出现冲突、空缺和无解等现象。
Genetic algorithms (GAs) are search algorithms based on of natural evolution processing including selection, mutation and crossover operations on the genes of individuals or potential solutions.
遗传算法是一种借鉴生物界自然选择和自然进化机制的搜索方法，通过对个体进行复制、交叉、变异操作完成搜索过程。
Genetic algorithms (GAs) are search algorithms based on of natural evolution processing including selection, mutation and crossover operations on the genes of individuals or potential solutions.
遗传算法是一种借鉴生物界自然选择和自然进化机制的搜索方法，通过对个体进行复制、交叉、变异操作完成搜索过程。
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