The GKLS generator is one of the most used testbeds for benchmarking global optimization algorithms. In this paper, we conduct both a computational analysis and the Exploratory Landscape Analysis (ELA) of the GKLS generator. We utilize both canonically used and newly generated classes of GKLS-generated problems and show their use in benchmarking three state-of-the-art methods (from evolutionary and deterministic communities) in dimensions 5 and 10. We show that the GKLS generator produces ``needle in a haystack'' type problems that become extremely difficult to optimize in higher dimensions. Furthermore, we conduct the ELA on the GKLS generator and then compare it to the ELA of two other widely used benchmark sets (BBOB and CEC 2014), and discuss the meaningfulness of the results.

翻译：GKLS生成器是全局优化算法基准测试最常用的测试平台之一。本文对GKLS生成器进行了计算分析与探索性景观分析（ELA）。我们采用了规范使用的和新生成的GKLS问题类别，展示了其在5维和10维空间中对三种最先进方法（分别来自进化计算和确定性算法领域）进行基准测试的效果。研究表明，GKLS生成器产生“大海捞针”型问题，这类问题在高维度下变得极其难以优化。此外，我们对GKLS生成器进行了ELA分析，并将其与另外两个广泛使用的基准测试集（BBOB和CEC 2014）的ELA结果进行比较，同时讨论了结果的实践意义。