mlsanityData to 7 Oct 2026

A Survey of Methods for Automated Algorithm Configuration

arXiv 2202.01651 ↗First posted 3 Feb 2022cs.AI cs.SEcited by 29

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Abstract (arXiv)

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 40 of its 218 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

40 of 218 references.

References · 40

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
  3. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 181
  4. 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
  5. 2020Hyper-Parameter Optimization: A Review of Algorithms and Applicationscited by 114
  6. 2018Automated Algorithm Selection: Survey and Perspectivescited by 111
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  8. 2014ParamILS: An Automatic Algorithm Configuration Frameworkcited by 88
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  11. 2019Automated Machine Learning: State-of-The-Art and Open Challengescited by 29
  12. 2021DACBench: A Benchmark Library for Dynamic Algorithm Configurationcited by 28
28 more
  1. 2019How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm designcited by 27
  2. 2017Pitfalls and Best Practices in Algorithm Configurationcited by 25
  3. 2020Learning Heuristic Selection with Dynamic Algorithm Configurationcited by 21
  4. 2017Warmstarting of Model-based Algorithm Configurationcited by 17
  5. 2018LeapsAndBounds: A Method for Approximately Optimal Algorithm Configurationcited by 13
  6. 2019Learning to Linkcited by 13
  7. 2020Refined bounds for algorithm configuration: The knife-edge of dual class approximabilitycited by 12
  8. 2018Automatic Construction of Parallel Portfolios via Explicit Instance Groupingcited by 11
  9. 2019Online Selection of CMA-ES Variantscited by 11
  10. 2020Extreme Algorithm Selection With Dyadic Feature Representationcited by 11
  11. 2021Algorithm Selection on a Meta Levelcited by 11
  12. 2019Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configurationcited by 9
  13. 2019On Performance Estimation in Automatic Algorithm Configurationcited by 8
  14. 2020Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysiscited by 8
  15. 2013Bayesian Optimization With Censored Response Datacited by 6
  16. 2020Neural Model-based Optimization with Right-Censored Observationscited by 6
  17. 2019Learning to Optimize Computational Resources: Frugal Training with Generalization Guaranteescited by 5
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  19. 2020Towards Meta-Algorithm Selectioncited by 5
  20. 2021Evolving Evolutionary Algorithms using Linear Genetic Programmingcited by 5
  21. 2019On the Impact of the Cutoff Time on the Performance of Algorithm Configuratorscited by 3
  22. 2021Machine Learning for Online Algorithm Selection under Censored Feedbackcited by 3
  23. 2020Online Preselection with Context Information under the Plackett-Luce Modelcited by 2
  24. 2020Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operatorscited by 2
  25. 2020MATE: A Model-based Algorithm Tuning Enginecited by 2
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 7; 2024: 8; 2025: 6; 2026: 5.

29 papers.

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  1. 2023Hyperparameters in Reinforcement Learning and How To Tune Themcited by 58
  2. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
  3. 2023Automated Design of Metaheuristic Algorithms: A Surveycited by 12
  4. 2024Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Processcited by 11
  5. 2024Explainable Benchmarking for Iterative Optimization Heuristicscited by 10
  6. 2024Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problemscited by 9
  7. 2024Automated Metaheuristic Algorithm Design with Autoregressive Learningcited by 7
  8. 2022AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configurationcited by 6
  9. 2022Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budgetcited by 5
  10. 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
  11. 2023Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initializecited by 3
  12. 2024ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learningcited by 3
17 more
  1. 2024Accelerating process control and optimization via machine learning: A reviewcited by 3
  2. 2024Regularization in Spider-Style Strategy Discovery and Schedule Constructioncited by 2
  3. 2026Scalable Dynamic Distributed Constraint Optimization with Metareasoning and Application to Continual Satellite Operationscited by 2
  4. 2026From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priorscited by 2
  5. 2022UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learningcited by 1
  6. 2023AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Designcited by 1
  7. 2023Discovering Multiple Algorithm Configurationscited by 1
  8. 2023LEO: Learning Efficient Orderings for Multiobjective Binary Decision Diagramscited by 1
  9. 2025Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Predictioncited by 1
  10. 2025Deep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the (1+(λ,λ))-GAcited by 1
  11. 2026GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMscited by 1
  12. 2024Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasetscited by 0
  13. 2025A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needscited by 0
  14. 2025A Review on Single-Problem Multi-Attempt Heuristic Optimizationcited by 0
  15. 2025Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Networkcited by 0
  16. 2026Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimizationcited by 0
  17. 2026DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformancecited by 0