节俭型贝叶斯优化：面向数据与资源受限发现的可替代代理模型

中文摘要 AI 辅助

贝叶斯优化（BO）广泛应用于科学与工程领域的数据高效优化，但其计算成本却很少与优化性能一同评估。本文开展了一项系统的、感知计算的BO研究，从优化质量与计算节俭性两个维度评估代理模型。在涵盖材料科学、力学、机器人学、化学和机器学习的8个基准函数及9个真实世界数据集上，我们对4种代理模型（高斯过程、随机森林、NGBoost、贝叶斯自适应样条曲面）进行了基准测试。结果表明，基于高斯过程的BO始终产生最高的时间与内存开销，却未提供更优的优化或样本效率；相比之下，可扩展替代方案在仅需一小部分计算成本的情况下实现了相当或更优的性能。基于这些发现，我们提出了一个代理推荐框架，可从低成本数据集特征中预测最适合的BO代理。综合来看，这些结果将FruBO确立为可复现的、感知计算的贝叶斯优化基线，并为计算与实验预算有限情况下的代理选择提供了实用指导。

英文摘要

Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance. Here we present a systematic, compute-aware study of BO that evaluates surrogate models along two axes: optimization quality and computational frugality. Across eight benchmark functions and nine real-world datasets spanning materials science, mechanics, robotics, chemistry, and machine learning, we benchmark four surrogate models: Gaussian Processes, Random Forests, NGBoost, and Bayesian Adaptive Spline Surfaces. We show that Gaussian Process-based BO consistently incurs the highest time and memory overhead without delivering superior optimization or sample efficiency. In contrast, scalable alternatives achieve equal or better performance at a fraction of the computational cost. Motivated by these findings, we introduce a surrogate-recommendation framework that predicts the most suitable BO surrogate from inexpensive dataset characteristics. Together, these results establish FruBO as a reproducible, compute-aware baseline for Bayesian Optimization and provide practical guidance for surrogate selection under limited computational and experimental budgets.

发表机构

• National Centre for Scientific Research "Demokritos"（希腊德谟克利特国家科学研究中心）
• University of Patras（帕特雷大学）
• National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）
• Hellenic Mediterranean University（希腊地中海大学）
• SciFY PNPC

机构由 AI 辅助整理，请以论文原文为准。