Efficient Sampling When Searching for Robust Solutions
Efficient Sampling When Searching for Robust Solutions
复制标题
寻找稳健的解决方案时进行高效采样
DOI：
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复制
发表时间：
2016
期刊：
影响因子：
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通讯作者：
Xin Fei
中科院分区：
文献类型：
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作者：
J. Branke;Xin Fei
在决策变量上存在噪声的情况下，通常期望找到鲁棒的解决方案，即，在可能的扰动分布上具有良好预期适应性的解。采样通常用于估计解决方案的预期适应度;然而，此选项可能在计算上昂贵。因此，研究人员建议考虑以前评估的解决方案中的信息。在本文中，我们假设每个解决方案进行评估一次，所有以前评估的解决方案的信息存储在一个内存中，可用于估计解决方案的预期适应度。然后，我们提出了一种新的方法，确定哪个解决方案应该进行评估，以最好地补充来自内存的信息，并分配权重，以估计从内存的解决方案的预期适应度。所提出的方法是基于Wasserstein距离，一个概率距离度量，衡量样本分布和所需的目标分布之间的差异。最后，我们提出的方法与其他文献中的抽样方法的实证比较，以证明我们的方法的有效性。
In the presence of noise on the decision variables, it is often desirable to find robust solutions, i.e., solutions with a good expected fitness over the distribution of possible disturbances. Sampling is commonly used to estimate the expected fitness of a solution; however, this option can be computationally expensive. Researchers have therefore suggested to take into account information from previously evaluated solutions. In this paper, we assume that each solution is evaluated once, and that the information about all previously evaluated solutions is stored in a memory that can be used to estimate a solution’s expected fitness. Then, we propose a new approach that determines which solution should be evaluated to best complement the information from the memory, and assigns weights to estimate the expected fitness of a solution from the memory. The proposed method is based on the Wasserstein distance, a probability distance metric that measures the difference between a sample distribution and a desired target distribution. Finally, an empirical comparison of our proposed method with other sampling methods from the literature is presented to demonstrate the efficacy of our method.