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Optimal design of large-scale screening experiments: a critical look at the coordinate-exchange algorithm

机译：大规模筛选实验的优化设计：对坐标交换算法的批判性观察

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摘要

We focus on the D-optimal design of screening experiments involving main-effects regression models, especially with large numbers of factors and observations. We propose a newselection strategy for the coordinate-exchange algorithm based on an orthogonality measure of the design. Computational experiments show that this strategy finds better designs within an execution time that is 30% shorter than other strategies. We also provide strong evidence that the use of the prediction variance as a selection strategy does not provide any added value in comparison to simpler selection strategies. Additionally, we propose a new iterated local search algorithm for the construction of D-optimal experimental designs. This new algorithm outperforms the original coordinate-exchange algorithm.
机译：我们专注于筛选实验的D最优设计，该筛选实验涉及主要效果回归模型，尤其是具有大量因素和观察结果的情况。我们基于设计的正交性度量为坐标交换算法提出了一种新的选择策略。计算实验表明，该策略在比其他策略短30％的执行时间内找到了更好的设计。我们还提供有力的证据，与简单的选择策略相比，将预测方差用作选择策略不会提供任何附加值。此外，我们提出了一种新的迭代局部搜索算法来构建D最优实验设计。此新算法优于原始的坐标交换算法。

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