A Less Uncertain Sampling-Based Method of Batch Bayesian Optimization

TL;DR

This paper introduces SCO, a sampling-based approach for batch Bayesian optimization that reduces uncertainty and improves solution quality without constructing high-dimensional acquisition functions.

Contribution

The paper proposes SCO, a novel sampling-computation-optimization method that enhances batch Bayesian optimization by reducing uncertainty and increasing flexibility compared to existing methods.

Findings

SCO produces less uncertain batch designs than other sampling-based methods.

SCO achieves better solutions than comparable batch methods in the same dimension and batch size.

SCO is adaptable to various one-site acquisition functions.

Abstract

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.