Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

authored by

Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr

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.

Organisation(s)

Machine Learning Section

External Organisation(s)

Sorbonne Université
Technical University of Munich (TUM)

Type

Paper

Publication date

17.11.2022

Publication status

Published

Peer reviewed

Yes

Electronic version(s)

https://openreview.net/forum?id=cmxtTF_IHd (Access: Open)