  Efficient solving of an unseen optimization problem is related to appropriate
selection of an optimization algorithm and its hyper-parameters. For this
purpose, automated algorithm performance prediction should be performed that in
most commonly-applied practices involves training a supervised ML algorithm
using a set of problem landscape features. However, the main issue of training
such models is their limited explainability since they only provide information
about the joint impact of the set of landscape features to the end prediction
results. In this study, we are investigating explainable landscape-aware
regression models where the contribution of each landscape feature to the
prediction of the optimization algorithm performance is estimated on a global
and local level. The global level provides information about the impact of the
feature across all benchmark problems' instances, while the local level
provides information about the impact on a specific problem instance. The
experimental results are obtained using the COCO benchmark problems and three
differently configured modular CMA-ESs. The results show a proof of concept
that different set of features are important for different problem instances,
which indicates that further personalization of the landscape space is required
when training an automated algorithm performance prediction model.
