# Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler

arXiv: https://arxiv.org/abs/2404.15837 · HTML: https://mlsanity.com/paper/2404.15837/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Multi-parameter Control for the (1+(λ,λ))-GA on OneMax via Deep Reinforcement Learning](https://mlsanity.com/paper/2505.12982/) (2025)
- [Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning](https://mlsanity.com/paper/2606.10129/) (2026)

## Abstract (arXiv)

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.