Details
| Originalsprache | Englisch |
|---|---|
| Publikationsstatus | Veröffentlicht - 17 Nov. 2022 |
| Veranstaltung | Workshop on Meta-Learning (MetaLearn 2022) - online Dauer: 2 Dez. 2022 → … |
Workshop
| Workshop | Workshop on Meta-Learning (MetaLearn 2022) |
|---|---|
| Zeitraum | 2 Dez. 2022 → … |
Abstract
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2022. Beitrag in Workshop on Meta-Learning (MetaLearn 2022).
Publikation: Konferenzbeitrag › Paper › Forschung › Peer-Review
}
TY - CONF
T1 - Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
AU - Benjamins, Carolin
AU - Jankovic, Anja
AU - Raponi, Elena
AU - Blom, Koen van der
AU - Lindauer, Marius
AU - Doerr, Carola
PY - 2022/11/17
Y1 - 2022/11/17
N2 - 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.
AB - 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.
KW - cs.LG
M3 - Paper
T2 - Workshop on Meta-Learning (MetaLearn 2022)
Y2 - 2 December 2022
ER -