The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features. These features can be used as input for ML models that are trained to select or to configure a suitable algorithm for the problem at hand. Since in pure black-box optimization information about the problem instance can only be obtained through function evaluation, a common approach is to dedicate some function evaluations for feature extraction, e.g., using random sampling. This approach has two key downsides: (1) It reduces the budget left for the actual optimization phase, and (2) it neglects valuable information that could be obtained from a problem-solver interaction. In this paper, we propose a feature extraction method that describes the trajectories of optimization algorithms using simple descriptive statistics. We evaluate the generated features for the task of classifying problem classes from the Black Box Optimization Benchmarking (BBOB) suite. We demonstrate that the proposed DynamoRep features capture enough information to identify the problem class on which the optimization algorithm is running, achieving a mean classification accuracy of 95% across all experiments.

翻译：机器学习模型在优化算法分析中的应用需要利用数值特征来表示优化问题。这些特征可作为输入，供训练用于为当前问题选择或配置合适算法的ML模型使用。由于在纯黑箱优化中，问题实例的信息只能通过函数评估获取，常见做法是预留部分函数评估用于特征提取（例如通过随机采样）。该方法存在两个关键缺陷：（1）减少了实际优化阶段的预算；（2）忽略了可通过问题-求解器交互获得的有价值信息。本文提出一种基于简单描述性统计量描述优化算法轨迹的特征提取方法。我们基于黑箱优化基准测试套件生成的特征，对问题类别分类任务进行评估。实验表明，所提出的DynamoRep特征能捕获足够信息，以识别优化算法运行的问题类别，在所有实验中平均分类准确率达到95%。