Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

TL;DR

This paper proposes a method for automatically selecting the acquisition function in Bayesian optimization using exploratory landscape analysis features, leading to improved performance over static choices on benchmark problems.

Contribution

It introduces a dynamic, data-driven approach for choosing acquisition functions in Bayesian optimization, enhancing adaptability and performance.

Findings

Random forest models can effectively recommend acquisition functions.

Dynamic selection outperforms static choices on BBOB benchmarks.

AutoML techniques can improve Bayesian optimization design.

Abstract

Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na\"ive random forest regression model, built on top of exploratory landscape analysis features that are computed from the initial design points, suffices to recommend AFs that outperform any static choice, when considering performance over the classic BBOB benchmark suite for derivative-free numerical optimization methods on the COCO platform. Our work hence paves a way towards AutoML-assisted, on-the-fly BO designs that adjust their behavior on a run-by-run basis.