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.

翻译：近期，动态环境下的优化问题成为多项理论研究的主题。其中单调动态二值问题在理论上能够有效区分不同遗传算法的性能差异。基于这一理论基础，我们将该问题的多个变体集成至IOHprofiler基准测试框架中。通过该集成平台，我们开展了一系列大规模基准实验，既验证了中等维度问题上的理论结果，又探究了尚未被理论研究的遗传算法性能特征。研究结果揭示了理论分析与基准测试之间的多重协同效应，并为动态优化问题的后续研究提供了实验平台。