mlsanityData to 7 Oct 2026

Using Automated Algorithm Configuration for Parameter Control

arXiv 2302.12334 ↗First posted 23 Feb 2023cs.LG cs.NEcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 23 of its 58 references that are in the map. First posted on arXiv 23 Feb 2023.

Builds on

23 of 58 references.

References · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  3. 2017Deep Reinforcement Learning that Matterscited by 727
  4. 2016COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Settingcited by 135
  5. 2019Deep Reinforcement Learning Based Parameter Control in Differential Evolutioncited by 37
  6. 2022Automated Dynamic Algorithm Configurationcited by 37
  7. 2022Multi-agent Dynamic Algorithm Configurationcited by 36
  8. 2020Benchmarking in Optimization: Best Practice and Open Issuescited by 35
  9. 2017Fast Genetic Algorithmscited by 29
  10. 2021DACBench: A Benchmark Library for Dynamic Algorithm Configurationcited by 28
  11. 2020Learning Heuristic Selection with Dynamic Algorithm Configurationcited by 21
  12. 2022Per-run Algorithm Selection with Warm-starting using Trajectory-based Featurescited by 20
11 more
  1. 2018Better Runtime Guarantees Via Stochastic Dominationcited by 12
  2. 2021Reproducibility in Evolutionary Computationcited by 12
  3. 2022Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configurationcited by 11
  4. 2020A Rigorous Runtime Analysis of the (1 + (λ, λ)) GA on Jump Functionscited by 8
  5. 2017Runtime Analysis of the (1+(λ,λ)) Genetic Algorithm on Random Satisfiable 3-CNF Formulascited by 7
  6. 2022Reinforcement learning based adaptive metaheuristicscited by 7
  7. 2019Hyper-Parameter Tuning for the (1+(λ,λ)) GAcited by 4
  8. 2020Optimal Mutation Rates for the (1+λ) EA on OneMaxcited by 4
  9. 2019Maximizing Drift is Not Optimal for Solving OneMaxcited by 3
  10. 2021Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMaxcited by 2
  11. 2021Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithmscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 3; 2026: 1.

5 papers.

  1. 2023AutoRL Hyperparameter Landscapescited by 8
  2. 2025On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GAcited by 5
  3. 2025Multi-parameter Control for the (1+(λ,λ))-GA on OneMax via Deep Reinforcement Learningcited by 1
  4. 2025Deep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the (1+(λ,λ))-GAcited by 1
  5. 2026Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learningcited by 0