# Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices

arXiv: https://arxiv.org/abs/1804.05650 · HTML: https://mlsanity.com/paper/1804.05650/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [Automated Reinforcement Learning (AutoRL): A Survey and Open Problems](https://mlsanity.com/paper/2201.03916/) (2022)
- [Automated Dynamic Algorithm Configuration](https://mlsanity.com/paper/2205.13881/) (2022)
- [How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs](https://mlsanity.com/paper/2304.10414/) (2023)
- [Self-Adjusting Weighted Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2306.04262/) (2023)
- [The (1+(λ,λ)) Global SEMO Algorithm](https://mlsanity.com/paper/2210.03618/) (2022)
- [From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms](https://mlsanity.com/paper/2206.09090/) (2022)
- [Multi-parameter Control for the (1+(λ,λ))-GA on OneMax via Deep Reinforcement Learning](https://mlsanity.com/paper/2505.12982/) (2025)
- [Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems](https://mlsanity.com/paper/2605.29916/) (2026)
- [Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning](https://mlsanity.com/paper/2606.10129/) (2026)

## Abstract (arXiv)

Parameter control aims at realizing performance gains through a dynamic choice of the parameters which determine the behavior of the underlying optimization algorithm. In the context of evolutionary algorithms this research line has for a long time been dominated by empirical approaches. With the significant advances in running time analysis achieved in the last ten years, the parameter control question has become accessible to theoretical investigations. A number of running time results for a broad range of different parameter control mechanisms have been obtained in recent years. This book chapter surveys these works, and puts them into context, by proposing an updated classification scheme for parameter control.