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.

本文提出了一种扩展的基于多个仿射组合的 MA-BBOB 生成器方法，以帮助填充实例空间，并探究其对算法选择价值的影响，而 MA-BBOB 建立在公开的 IOHprofiler 平台上，可实现标准实验程序，提供性能分析和可视化，并与 (MA-) BBOB 函数的数据集进行比较。