Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
This work offers an incremental improvement for researchers and practitioners in optimization by enabling automated, adaptive BO designs.
The authors tackled the problem of improving Bayesian optimization (BO) by dynamically selecting acquisition functions, showing that a random forest model using exploratory landscape analysis features from initial design points outperforms static choices on the BBOB benchmark suite.
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.