When designing a benchmark problem set, it is important to create a set of benchmark problems that are a good generalization of the set of all possible problems. One possible way of easing this difficult task is by using artificially generated problems. In this paper, one such single-objective continuous problem generation approach is analyzed and compared with the COCO benchmark problem set, a well know problem set for benchmarking numerical optimization algorithms. Using Exploratory Landscape Analysis and Singular Value Decomposition, we show that such representations allow us to further explore the relations between the problems by applying visualization and correlation analysis techniques, with the goal of decreasing the bias in benchmark problem assessment.

中文翻译：

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COCO基准问题与人为产生的问题的互补性分析

在设计基准问题集时，重要的是要创建一组基准问题，以很好地概括所有可能出现的问题。减轻此困难任务的一种可能方法是使用人为产生的问题。在本文中，分析了一种这样的单目标连续问题生成方法，并将其与COCO基准问题集（基准数值优化算法的基准问题集）进行了比较。使用探索性景观分析和奇异值分解，我们证明了这些表示允许我们通过应用可视化和相关分析技术来进一步探索问题之间的关系，目的是减少基准问题评估中的偏差。