论文标题
对基于模型的自适应抽样的经验评论，用于全球优化昂贵的黑盒功能
An Empirical Review of Model-based Adaptive Sampling for Global Optimization of Expensive Black-box Functions
论文作者
论文摘要
本文回顾了基于最先进的模型的自适应采样方法，用于单目标黑盒优化（BBO）。尽管BBO文献包括各种有希望的抽样技术，但仍缺乏对BBO问题范围内现有研究的全面研究。我们首先将BBO问题分为两类：工程设计和算法设计优化，并讨论其挑战。然后，我们批判性地讨论和分析了针对关键采集功能的基于自适应模型的抽样技术。我们详细介绍了工程设计问题的基于方差的抽样技术的缺点。此外，我们提供了有关离散方案对采集功能绩效的影响的深入见解。我们强调动态离散化对基于距离的探索的重要性，并引入EEPA+，这是先前提出的基于帕累托的采样技术的改进变体。我们的经验分析揭示了基于方差的技术对算法设计和基于距离的工程设计优化问题的有效性。
This paper reviews the state-of-the-art model-based adaptive sampling approaches for single-objective black-box optimization (BBO). While BBO literature includes various promising sampling techniques, there is still a lack of comprehensive investigations of the existing research across the vast scope of BBO problems. We first classify BBO problems into two categories: engineering design and algorithm design optimization and discuss their challenges. We then critically discuss and analyze the adaptive model-based sampling techniques focusing on key acquisition functions. We elaborate on the shortcomings of the variance-based sampling techniques for engineering design problems. Moreover, we provide in-depth insights on the impact of the discretization schemes on the performance of acquisition functions. We emphasize the importance of dynamic discretization for distance-based exploration and introduce EEPA+, an improved variant of a previously proposed Pareto-based sampling technique. Our empirical analyses reveal the effectiveness of variance-based techniques for algorithm design and distance-based methods for engineering design optimization problems.
