Bayesian optimization (BO) developed as an approach for the efficient optimization of expensive black-box functions without gradient information. A typical BO paper introduces a new approach and compares it to some alternatives on simulated and possibly real examples to show its efficacy. Yet on a different example, this new algorithm might not be as effective as the alternatives. This paper looks at a broader family of approaches to explain the strengths and weaknesses of algorithms in the family, with guidance on what choices might work best on different classes of problems.

翻译：贝叶斯优化（BO）作为一种无需梯度信息即可高效优化昂贵黑箱函数的方法而发展起来。典型的BO论文会提出一种新方法，并在模拟实例及可能的真实实例上将其与若干替代方案进行比较，以证明其有效性。然而，在不同的实例上，这种新算法可能不如替代方案有效。本文着眼于一个更广泛的方法家族，解释该家族中各算法的优势与劣势，并就针对不同类别问题哪些选择可能效果最佳提供指导。