In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.

中文翻译：

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结合探索性景观分析和机器学习的连续黑盒问题的自动算法选择

在本文中，我们基于之前关于设计信息丰富且高效的探索性景观分析特征以表征问题景观的工作，并展示了它们在连续黑盒优化问题中自动构建算法选择模型的有效性。围绕 COCO 平台几年来的算法性能结果，我们构建了一组具有代表性的高性能互补求解器，并提出了一个算法选择模型，与投资组合的单个最佳求解器相比，平均需要不到一半的资源来解决解决给定的问题。因此，与经典的集成方法相比，效率有了巨大的提高，并且通过使用信息特征可以增加对问题特征和算法属性的洞察力。该模型假设黑盒优化基准的函数集对实际应用具有足够的代表性。该模型允许在优化之前基于函数评估的小样本，在考虑的集合中为看不见的问题选择最合适的优化算法。请注意，这样的样本甚至可以重复用于进化（优化）算法的初始群体，因此即使特征成本也可以忽略不计。