Self-Adjusting Weighted Expected Improvement for Bayesian Optimization

TL;DR

This paper introduces SAWEI, a self-adjusting acquisition function for Bayesian Optimization that dynamically balances exploration and exploitation based on convergence, improving robustness and performance across different black-box optimization problems.

Contribution

We propose SAWEI, a novel data-driven, self-adjusting acquisition function for Bayesian Optimization that adapts exploration-exploitation trade-off during the optimization process.

Findings

SAWEI outperforms handcrafted baselines on BBOB functions.

SAWEI demonstrates robust performance on HPOBench.

The method adapts sampling behavior automatically to problem structure.

Abstract

Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is highly configurable with many different design choices regarding the initial design, surrogate model, and acquisition function (AF). Unfortunately, our understanding of how to select suitable components for a problem at hand is very limited. In this work, we focus on the definition of the AF, whose main purpose is to balance the trade-off between exploring regions with high uncertainty and those with high promise for good solutions. We propose Self-Adjusting Weighted Expected Improvement (SAWEI), where we let the exploration-exploitation trade-off self-adjust in a data-driven manner, based on a convergence criterion for BO. On the noise-free black-box BBOB functions of the COCO benchmarking platform, our method exhibits a favorable any-time performance compared to handcrafted baselines and serves as a robust default choice for any problem structure. The suitability of our method also transfers to HPOBench. With SAWEI, we are a step closer to on-the-fly, data-driven, and robust BO designs that automatically adjust their sampling behavior to the problem at hand.