This study examines the generalization ability of algorithm performance
prediction models across various benchmark suites. Comparing the statistical
similarity between the problem collections with the accuracy of performance
prediction models that are based on exploratory landscape analysis features, we
observe that there is a positive correlation between these two measures.
Specifically, when the high-dimensional feature value distributions between
training and testing suites lack statistical significance, the model tends to
generalize well, in the sense that the testing errors are in the same range as
the training errors. Two experiments validate these findings: one involving the
standard benchmark suites, the BBOB and CEC collections, and another using five
collections of affine combinations of BBOB problem instances.

该研究通过对各种基准套件的算法性能预测模型的泛化能力进行考察，比较问题集合的统计相似性和基于探索性景观分析特征的性能预测模型的准确性，我们发现这两个指标之间存在着正相关关系。具体来说，当训练和测试套件之间的高维特征值分布缺乏统计显著性时，模型往往能够很好地进行泛化，即测试误差与训练误差处于同一范围内。两个实验证实了这些发现：一个涉及标准基准套件 BBOB 和 CEC 集合，另一个使用了五个由 BBOB 问题实例的仿射组合构成的集合。