论文标题
批处理贝叶斯优化的基于不确定的采样方法
A Less Uncertain Sampling-Based Method of Batch Bayesian Optimization
论文作者
论文摘要
本文介绍了一种称为“采样 - 计算优化”（SCO）的方法，以设计批次贝叶斯优化。 SCO不会构建新的高维采集函数，而是从现有的一站采集函数中构建样本以获取几个候选样本。为了减少采样的不确定性，计算总体差异以比较这些样本。最后，遗传算法和开关算法用于优化设计。几种策略用于减轻SCO中的计算负担。从数值结果中，SCO设计不如其他基于采样的方法的设计不确定。至于在批处理贝叶斯优化中应用，与其他尺寸和批处理大小的其他批处理方法相比，SCO可以找到更好的解决方案。另外，它也是灵活的，并且可以适用于不同的一个站点方法。最后，给出了一个复杂的实验案例，以说明SCO方法的应用值和方案。
This paper presents a method called sampling-computation-optimization (SCO) to design batch Bayesian optimization. SCO does not construct new high-dimensional acquisition functions but samples from the existing one-site acquisition function to obtain several candidate samples. To reduce the uncertainty of the sampling, the general discrepancy is computed to compare these samples. Finally, the genetic algorithm and switch algorithm are used to optimize the design. Several strategies are used to reduce the computational burden in the SCO. From the numerical results, the SCO designs were less uncertain than those of other sampling-based methods. As for application in batch Bayesian optimization, SCO can find a better solution when compared with other batch methods in the same dimension and batch size. In addition, it is also flexible and can be adapted to different one-site methods. Finally, a complex experimental case is given to illustrate the application value and scenario of SCO method.
