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”优化顺序方法（包括贝叶斯优化技术）在对特定目标函数进行优化时，往往会重复访问相同的数据点，导致需要大量步骤才能找到全局最优解。本文通过数值研究，探讨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。我们发现，当采集函数采用最大后验估计时，该后处理方法能显著减少寻找全局最优解所需的顺序步骤。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问题，提供了一种简单而通用的策略。