# Using Automated Algorithm Configuration for Parameter Control

arXiv: https://arxiv.org/abs/2302.12334 · HTML: https://mlsanity.com/paper/2302.12334/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Automated Dynamic Algorithm Configuration](https://mlsanity.com/paper/2205.13881/) (2022)
- [Deep Reinforcement Learning Based Parameter Control in Differential Evolution](https://mlsanity.com/paper/1905.08006/) (2019)
- [Benchmarking in Optimization: Best Practice and Open Issues](https://mlsanity.com/paper/2007.03488/) (2020)
- [Multi-agent Dynamic Algorithm Configuration](https://mlsanity.com/paper/2210.06835/) (2022)
- [Fast Genetic Algorithms](https://mlsanity.com/paper/1703.03334/) (2017)
- [DACBench: A Benchmark Library for Dynamic Algorithm Configuration](https://mlsanity.com/paper/2105.08541/) (2021)
- [Learning Heuristic Selection with Dynamic Algorithm Configuration](https://mlsanity.com/paper/2006.08246/) (2020)
- [Per-run Algorithm Selection with Warm-starting using Trajectory-based Features](https://mlsanity.com/paper/2204.09483/) (2022)
- [Better Runtime Guarantees Via Stochastic Domination](https://mlsanity.com/paper/1801.04487/) (2018)
- [Reproducibility in Evolutionary Computation](https://mlsanity.com/paper/2102.03380/) (2021)
- [Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration](https://mlsanity.com/paper/2202.03259/) (2022)
- [A Rigorous Runtime Analysis of the (1 + (λ, λ)) GA on Jump Functions](https://mlsanity.com/paper/2004.06702/) (2020)
- [Runtime Analysis of the (1+(λ,λ)) Genetic Algorithm on Random Satisfiable 3-CNF Formulas](https://mlsanity.com/paper/1704.04366/) (2017)
- [Reinforcement learning based adaptive metaheuristics](https://mlsanity.com/paper/2206.12233/) (2022)
- [Hyper-Parameter Tuning for the (1+(λ,λ)) GA](https://mlsanity.com/paper/1904.04608/) (2019)
- [Optimal Mutation Rates for the (1+λ) EA on OneMax](https://mlsanity.com/paper/2006.11457/) (2020)
- [Maximizing Drift is Not Optimal for Solving OneMax](https://mlsanity.com/paper/1904.07818/) (2019)
- [Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMax](https://mlsanity.com/paper/2102.04944/) (2021)
- [Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms](https://mlsanity.com/paper/2102.11461/) (2021)

## Built on by (most cited first, 5 of 5)

- [AutoRL Hyperparameter Landscapes](https://mlsanity.com/paper/2304.02396/) (2023)
- [On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GA](https://mlsanity.com/paper/2502.20265/) (2025)
- [Multi-parameter Control for the (1+(λ,λ))-GA on OneMax via Deep Reinforcement Learning](https://mlsanity.com/paper/2505.12982/) (2025)
- [Deep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the (1+(λ,λ))-GA](https://mlsanity.com/paper/2512.03805/) (2025)
- [Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning](https://mlsanity.com/paper/2606.10129/) (2026)

## Abstract (arXiv)

Dynamic Algorithm Configuration (DAC) tackles the question of how to automatically learn policies to control parameters of algorithms in a data-driven fashion. This question has received considerable attention from the evolutionary community in recent years. Having a good benchmark collection to gain structural understanding on the effectiveness and limitations of different solution methods for DAC is therefore strongly desirable. Following recent work on proposing DAC benchmarks with well-understood theoretical properties and ground truth information, in this work, we suggest as a new DAC benchmark the controlling of the key parameter λ in the (1+(λ,λ))~Genetic Algorithm for solving OneMax problems. We conduct a study on how to solve the DAC problem via the use of (static) automated algorithm configuration on the benchmark, and propose techniques to significantly improve the performance of the approach. Our approach is able to consistently outperform the default parameter control policy of the benchmark derived from previous theoretical work on sufficiently large problem sizes. We also present new findings on the landscape of the parameter-control search policies and propose methods to compute stronger baselines for the benchmark via numerical approximations of the true optimal policies.