  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.
