  Optimization problems in dynamic environments have recently been the source
of several theoretical studies. One of these problems is the monotonic Dynamic
Binary Value problem, which theoretically has high discriminatory power between
different Genetic Algorithms. Given this theoretical foundation, we integrate
several versions of this problem into the IOHprofiler benchmarking framework.
Using this integration, we perform several large-scale benchmarking experiments
to both recreate theoretical results on moderate dimensional problems and
investigate aspects of GA's performance which have not yet been studied
theoretically. Our results highlight some of the many synergies between theory
and benchmarking and offer a platform through which further research into
dynamic optimization problems can be performed.
