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Optimal management of educational resources design based on genetic algorithm

机译:基于遗传算法的教育资源设计优化管理

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The spatial design of experiment and information subset had been studied by correlated random sequence sampling. The problems in many applied region had been found, such as statistical geology, sampling sequence, and environmental statistics. In all applications, maximum sampling sequence can be selected and different location and times. In maximum sampling sequence design, the feasible design program would be taken when the design domain and time are discrete from the design goal and expected result. The main problem is how to solve the maximum sampling sequence design idea. This is the algorithm GA sequence theory problem. In order to apply the GA design in computer experiments, in many cases, the design space is not possible to calculate accurately. In order to improve the efficient experiment of sampling sequence, the GA algorithm is developed to take advantage of Robustness power of sequence algorithm. The test results show that design idea and construction are very efficient for solving the mistake.
机译:通过相关随机序列抽样研究了实验和信息子集的空间设计。发现了许多应用区域中的问题,例如统计地质,采样顺序和环境统计。在所有应用中,可以选择最大采样顺序以及不同的位置和时间。在最大采样序列设计中,当设计域和时间与设计目标和预期结果不符时,将采用可行的设计程序。主要问题是如何解决最大采样序列的设计思想。这是算法GA序列理论的问题。为了将GA设计应用于计算机实验,在许多情况下,无法准确地计算设计空间。为了提高采样序列的效率,开发了遗传算法以利用序列算法的鲁棒性。测试结果表明,设计思路和构造方法对于解决该错误非常有效。

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