An important challenge in black-box optimization is to be able to understand the relative performance of different algorithms on problem instances. This challenge has motivated research in exploratory landscape analysis and algorithm selection, leading to a number of frameworks for analysis. However, these procedures often involve significant assumptions, or rely on information not typically available. In this paper we propose a new, model-based framework for the characterization of black-box optimization problems using Gaussian Process regression. The framework allows problem instances to be compared to each other in a relatively simple way. The model-based approach also allows us to assess the goodness of fit and Gaussian Processes lead to an efficient means of model comparison. The implementation of the framework is described and validated on several test sets as one benchmark problem is slowly transformed into another.

中文翻译：

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使用高斯过程进行黑盒问题比较的基于模型的框架。

黑盒优化的一个重要挑战是能够了解问题实例上不同算法的相对性能。这一挑战激发了探索性景观分析和算法选择方面的研究，从而形成了许多分析框架。但是，这些过程通常涉及重大假设，或者依赖于通常不可用的信息。在本文中，我们提出了一个基于模型的新框架，用于使用高斯过程回归来表征黑箱优化问题。该框架允许以相对简单的方式将问题实例相互比较。基于模型的方法还使我们能够评估拟合的优劣，而高斯过程导致了模型比较的有效手段。