  Accurately predicting the performance of different optimization algorithms
for previously unseen problem instances is crucial for high-performing
algorithm selection and configuration techniques. In the context of numerical
optimization, supervised regression approaches built on top of exploratory
landscape analysis are becoming very popular. From the point of view of Machine
Learning (ML), however, the approaches are often rather naive, using default
regression or classification techniques without proper investigation of the
suitability of the ML tools. With this work, we bring to the attention of our
community the possibility to personalize regression models to specific types of
optimization problems. Instead of aiming for a single model that works well
across a whole set of possibly diverse problems, our personalized regression
approach acknowledges that different models may suite different types of
problems. Going one step further, we also investigate the impact of selecting
not a single regression model per problem, but personalized ensembles. We test
our approach on predicting the performance of numerical optimization heuristics
on the BBOB benchmark collection.
