Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

verfasst von

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.

Organisationseinheit(en)

Fachgebiet Maschinelles Lernen

Externe Organisation(en)

Sorbonne Université
Technische Universität München (TUM)

Typ

Paper

Publikationsdatum

17.11.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

Elektronische Version(en)

https://openreview.net/forum?id=cmxtTF_IHd (Zugang: Offen)