论文标题
通过探索性景观分析实现贝叶斯优化的自动设计
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
论文作者
论文摘要
贝叶斯优化（BO）算法构成了一类基于替代的启发式方法，旨在有效地计算出数值黑盒优化问题的高质量解决方案。 Bo Pipeline是高度模块化的，针对初始采样策略，替代模型，采集功能（AF）具有不同的设计选择，用于优化AF等的求解器。我们在这项工作中证明了AF的动态选择可以使BO设计受益。更确切地说，我们表明，已经是一个幼稚的随机森林回归模型，该模型建立在探索性景观分析功能的基础上，这些功能是从初始设计点计算出来的，足以推荐AFS，在考虑经典的BBOB基准套件上的性能以胜过任何静态选择，以实现Coco平台上无衍生的无数值优化方法。因此，我们的工作为自动辅助，即时的BO设计铺平了一种方法，可以按跑步调整其行为。
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.
