Model-based sequential approaches to discrete "black-box" optimization, including Bayesian optimization techniques, often access the same points multiple times for a given objective function in interest, resulting in many steps to find the global optimum. Here, we numerically study the effect of a postprocessing method on Bayesian optimization that strictly prohibits duplicated samples in the dataset. We find the postprocessing method significantly reduces the number of sequential steps to find the global optimum, especially when the acquisition function is of maximum a posterior estimation. Our results provide a simple but general strategy to solve the slow convergence of Bayesian optimization for high-dimensional problems.

翻译：基于模型的离散"黑箱"优化序贯方法（包括贝叶斯优化技术）在针对特定目标函数时，常反复访问相同采样点，导致寻找全局最优解需要大量迭代步骤。本文通过数值实验研究了一种严格禁止数据集中重复样本的后处理方法对贝叶斯优化的影响。研究发现，该后处理方法能显著减少找到全局最优解所需的序贯步骤数量，尤其在采用最大后验估计采集函数时效果更为明显。我们的研究结果为解决高维问题中贝叶斯优化收敛缓慢的难题提供了一种简单而通用的策略。