  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.
