论文标题
犹豫的自适应搜索，估计和分位数自适应搜索全局优化，并使用噪声
Hesitant Adaptive Search with Estimation and Quantile Adaptive Search for Global Optimization with Noise
论文作者
论文摘要
自适应随机搜索方法已被证明对全局优化问题有效，在某些条件下，预期性能时间仅随维度线性增加。但是，以前的分析假设可以直接观察目标函数。我们考虑必须像模拟一样估算目标函数（通常使用嘈杂函数）的情况。我们提供了算法性能的有限时间分析，该分析将估计与采样分布相结合。我们提出了一个用估计的犹豫自适应搜索的框架，并在某些条件下在函数评估上得出了一个立方体的函数评估。我们通过估计将框架扩展到分位数自适应搜索，该估计集中了一系列嵌套分位数集的采样点。分析表明，比在自适应搜索算法进度期间对目标函数值的精炼估计值相比，计算努力更好地用于改进点。
Adaptive random search approaches have been shown to be effective for global optimization problems, where under certain conditions, the expected performance time increases only linearly with dimension. However, previous analyses assume that the objective function can be observed directly. We consider the case where the objective function must be estimated, often using a noisy function, as in simulation. We present a finite-time analysis of algorithm performance that combines estimation with a sampling distribution. We present a framework called Hesitant Adaptive Search with Estimation, and derive an upper bound on function evaluations that is cubic in dimension, under certain conditions. We extend the framework to Quantile Adaptive Search with Estimation, which focuses sampling points from a series of nested quantile level sets. The analyses suggest that computational effort is better expended on sampling improving points than refining estimates of objective function values during the progress of an adaptive search algorithm.
