MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating
  AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
Diederick Vermetten Furong Ye Thomas Bäck Carola Doerr
Extending a recent suggestion to generate new instances for numerical
black-box optimization benchmarking by interpolating pairs of the
well-established BBOB functions from the COmparing COntinuous Optimizers (COCO)
platform, we propose in this work a further generalization that allows multiple
affine combinations of the original instances and arbitrarily chosen locations
of the global optima. We demonstrate that the MA-BBOB generator can help fill
the instance space, while overall patterns in algorithm performance are
preserved. By combining the landscape features of the problems with the
performance data, we pose the question of whether these features are as useful
for algorithm selection as previous studies suggested. MA-BBOB is built on the
publicly available IOHprofiler platform, which facilitates standardized
experimentation routines, provides access to the interactive IOHanalyzer module
for performance analysis and visualization, and enables comparisons with the
rich and growing data collection available for the (MA-)BBOB functions.