  Selecting the most suitable algorithm and determining its hyperparameters for
a given optimization problem is a challenging task. Accurately predicting how
well a certain algorithm could solve the problem is hence desirable. Recent
studies in single-objective numerical optimization show that supervised machine
learning methods can predict algorithm performance using landscape features
extracted from the problem instances.
  Existing approaches typically treat the algorithms as black-boxes, without
consideration of their characteristics. To investigate in this work if a
selection of landscape features that depends on algorithms properties could
further improve regression accuracy, we regard the modular CMA-ES framework and
estimate how much each landscape feature contributes to the best algorithm
performance regression models. Exploratory data analysis performed on this data
indicate that the set of most relevant features does not depend on the
configuration of individual modules, but the influence that these features have
on regression accuracy does. In addition, we have shown that by using
classifiers that take the features relevance on the model accuracy, we are able
to predict the status of individual modules in the CMA-ES configurations.
