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.

翻译：通过扩展近期提出的在数值黑箱优化基准测试中生成新实例的方法——即对COCO（比较连续优化器）平台中成熟的BBOB函数进行成对插值，本文提出了一种更通用的推广方案，允许对原始实例进行多重仿射组合，并任意选择全局最优点的位置。我们证明MA-BBOB生成器有助于填充实例空间，同时保持算法性能的整体模式。通过将问题的景观特征与性能数据相结合，我们质疑这些特征是否如先前研究所建议的那样对算法选择具有同等效用。MA-BBOB基于公开的IOHprofiler平台构建，该平台支持标准化的实验流程，提供交互式IOHanalyzer模块用于性能分析与可视化，并能与（MA-）BBOB函数丰富且持续增长的数据集进行对比。