  Choosing a set of benchmark problems is often a key component of any
empirical evaluation of iterative optimization heuristics. In continuous,
single-objective optimization, several sets of problems have become widespread,
including the well-established BBOB suite. While this suite is designed to
enable rigorous benchmarking, it is also commonly used for testing methods such
as algorithm selection, which the suite was never designed around.
  We present the MA-BBOB function generator, which uses the BBOB suite as
component functions in an affine combination. In this work, we describe the
full procedure to create these affine combinations and highlight the trade-offs
of several design decisions, specifically the choice to place the optimum
uniformly at random in the domain. We then illustrate how this generator can be
used to gain more low-level insight into the function landscapes through the
use of exploratory landscape analysis.
  Finally, we show a potential use-case of MA-BBOB in generating a wide set of
training and testing data for algorithm selectors. Using this setup, we show
that the basic scheme of using a set of landscape features to predict the best
algorithm does not lead to optimal results, and that an algorithm selector
trained purely on the BBOB functions generalizes poorly to the affine
combinations.
