  The recently proposed MA-BBOB function generator provides a way to create
numerical black-box benchmark problems based on the well-established BBOB
suite. Initial studies on this generator highlighted its ability to smoothly
transition between the component functions, both from a low-level landscape
feature perspective, as well as with regard to algorithm performance. This
suggests that MA-BBOB-generated functions can be an ideal testbed for automated
machine learning methods, such as automated algorithm selection (AAS).
  In this paper, we generate 11800 functions in dimensions $d=2$ and $d=5$,
respectively, and analyze the potential gains from AAS by studying performance
complementarity within a set of eight algorithms. We combine this performance
data with exploratory landscape features to create an AAS pipeline that we use
to investigate how to efficiently select training sets within this space. We
show that simply using the BBOB component functions for training yields poor
test performance, while the ranking between uniformly chosen and
diversity-based training sets strongly depends on the distribution of the test
set.
