ForschungPublikationen
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

Kategorien Bayesian Optimization
Automated Machine Learning
Jahr 2022
Autoren Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr
Veröffentlicht in 6th Workshop on Meta-Learning at NeurIPS 2022, New Orleans
Beschreibung

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 naïve 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.