Computational and Exploratory Landscape Analysis of the GKLS Generator
This provides insights for researchers in optimization on benchmark selection, but is incremental as it extends existing analysis methods to a known generator.
The paper analyzed the GKLS generator for global optimization benchmarking, showing it creates extremely difficult 'needle in a haystack' problems in higher dimensions, and compared it to other benchmark sets using Exploratory Landscape Analysis.
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.