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生成器是用于评估全局优化算法的最常用测试平台之一。本文对GKLS生成器进行了计算分析和探索性景观分析(ELA)。我们使用经典和新生成的GKLS生成问题类别,展示它们在五维和十维中用于评估三种最先进的方法(采用进化和确定性算法)。我们发现,GKLS生成器产生的问题就像是大海捞针,远超高维度的优化难度。此外,我们进行了GKLS生成器的ELA,并将其与另外两个广泛使用的基准集(BBOB和CEC 2014)进行了比较,并探讨了结果的意义。