  Surrogate modeling has become a valuable technique for black-box optimization
tasks with expensive evaluation of the objective function. In this paper, we
investigate the relationship between the predictive accuracy of surrogate
models and features of the black-box function landscape. We also study
properties of features for landscape analysis in the context of different
transformations and ways of selecting the input data. We perform the landscape
analysis of a large set of data generated using runs of a surrogate-assisted
version of the Covariance Matrix Adaptation Evolution Strategy on the noiseless
part of the Comparing Continuous Optimisers benchmark function testbed.
