Exploratory Landscape Analysis provides sample-based methods to calculate features of black-box optimization problems in a quantitative and measurable way. Many problem features have been proposed in the literature in an attempt to provide insights into the structure of problem landscapes and to use in selecting an effective algorithm for a given optimization problem. While there has been some success, evaluating the utility of problem features in practice presents some significant challenges. Machine learning models have been employed as part of the evaluation process, but they may require additional information about the problems as well as having their own hyper-parameters, biases and experimental variability. As a result, extra layers of uncertainty and complexity are added into the experimental evaluation process, making it difficult to clearly assess the effect of the problem features. In this article, we propose a novel method for the evaluation of problem features which can be applied directly to individual or groups of features and does not require additional machine learning techniques or confounding experimental factors. The method is based on the feature's ability to detect a prior ranking of similarity in a set of problems. Analysis of Variance (ANOVA) significance tests are used to determine if the feature has successfully distinguished the successive problems in the set. Based on ANOVA test results, a percentage score is assigned to each feature for different landscape characteristics. Experimental results for twelve different features on four problem transformations demonstrate the method and provide quantitative evidence about the ability of different problem features to detect specific properties of problem landscapes.

中文翻译：

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使用问题转换的黑盒优化中的直接特征评估

探索性景观分析提供基于样本的方法，以定量和可衡量的方式计算黑盒优化问题的特征。文献中提出了许多问题特征，试图提供对问题格局结构的洞察，并用于为给定的优化问题选择有效的算法。虽然取得了一些成功，但在实践中评估问题特征的效用提出了一些重大挑战。机器学习模型已被用作评估过程的一部分，但它们可能需要有关问题的额外信息以及拥有自己的超参数、偏差和实验可变性。因此，在实验评估过程中增加了额外的不确定性和复杂性层，难以清楚地评估问题特征的影响。在本文中，我们提出了一种评估问题特征的新方法，该方法可以直接应用于单个或一组特征，并且不需要额外的机器学习技术或混淆实验因素。该方法基于特征检测一组问题中相似性的先验排名的能力。方差分析 (ANOVA) 显着性检验用于确定特征是否成功区分了集合中的连续问题。根据 ANOVA 测试结果，为不同景观特征的每个要素分配一个百分比分数。