Design sampling and replication assignment under fixed computing budget
Design sampling and replication assignment under fixed computing budget
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DOI：
10.1007/s11518-006-0195-6
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发表时间：
2005-09
影响因子：
1.2
通讯作者：
L. Lee;E. P. Chew
中科院分区：
文献类型：
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作者：
L. Lee;E. P. Chew
对于许多真实的问题，当设计空间是巨大的和非结构化的，并且需要耗时的仿真来估计性能度量时，重要的是要确定要采样多少设计以及对于每个设计方案运行多长时间，因为我们只有固定的计算时间。在本文中，我们对性能指标的分布和估计误差/噪声的分布如何影响决策进行了模拟研究。从分析中可以看出，当噪声的潜在分布有界时，如果我们很有可能获得最小的噪声，那么决策将是尽可能多地采样，但如果噪声是无界的，那么首先通过为每个设计分配更多的重复来降低噪声水平将是重要的。另一方面，如果性能指标的分布表明我们将有很高的机会得到好的设计，建议也降低噪音水平，否则，我们需要抽样更多的设计，以增加得到好的设计的机会。对于性能指标和噪声的分布均为正态分布的特殊情况，我们能够估计要采样的设计数量以及要运行的重复次数，以获得最佳性能。
For many real world problems, when the design space is huge and unstructured, and time consuming simulation is needed to estimate the performance measure, it is important to decide how many designs to sample and how long to run for each design alternative given that we have only a fixed amount of computing time. In this paper, we present a simulation study on how the distribution of the performance measures and distribution of the estimation errors/noises will affect the decision. From the analysis, it is observed that when the underlying distribution of the noise is bounded and if there is a high chance that we can get the smallest noise, then the decision will be to sample as many as possible, but if the noise is unbounded, then it will be important to reduce the noise level first by assigning more replications for each design. On the other hand, if the distribution of the performance measure indicates that we will have a high chance of getting good designs, the suggestion is also to reduce the noise level, otherwise, we need to sample more designs so as to increase the chances of getting good designs. For the special case when the distributions of both the performance measures and noise are normal, we are able to estimate the number of designs to sample, and the number of replications to run in order to obtain the best performance.