# A Survey of Methods for Automated Algorithm Configuration

arXiv: https://arxiv.org/abs/2202.01651 · HTML: https://mlsanity.com/paper/2202.01651/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 218 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 30 of 40)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Hyper-Parameter Optimization: A Review of Algorithms and Applications](https://mlsanity.com/paper/2003.05689/) (2020)
- [Preference-based Online Learning with Dueling Bandits: A Survey](https://mlsanity.com/paper/1807.11398/) (2018)
- [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014)
- [Learning to Branch](https://mlsanity.com/paper/1803.10150/) (2018)
- [Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems](https://mlsanity.com/paper/1611.04535/) (2016)
- [Automated Machine Learning: State-of-The-Art and Open Challenges](https://mlsanity.com/paper/1906.02287/) (2019)
- [DACBench: A Benchmark Library for Dynamic Algorithm Configuration](https://mlsanity.com/paper/2105.08541/) (2021)
- [How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design](https://mlsanity.com/paper/1908.02894/) (2019)
- [Pitfalls and Best Practices in Algorithm Configuration](https://mlsanity.com/paper/1705.06058/) (2017)
- [Learning Heuristic Selection with Dynamic Algorithm Configuration](https://mlsanity.com/paper/2006.08246/) (2020)
- [Warmstarting of Model-based Algorithm Configuration](https://mlsanity.com/paper/1709.04636/) (2017)
- [Learning to Link](https://mlsanity.com/paper/1907.00533/) (2019)
- [LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration](https://mlsanity.com/paper/1807.00755/) (2018)
- [Refined bounds for algorithm configuration: The knife-edge of dual class approximability](https://mlsanity.com/paper/2006.11827/) (2020)
- [Automatic Construction of Parallel Portfolios via Explicit Instance Grouping](https://mlsanity.com/paper/1804.06088/) (2018)
- [Online Selection of CMA-ES Variants](https://mlsanity.com/paper/1904.07801/) (2019)
- [Extreme Algorithm Selection With Dyadic Feature Representation](https://mlsanity.com/paper/2001.10741/) (2020)
- [Algorithm Selection on a Meta Level](https://mlsanity.com/paper/2107.09414/) (2021)
- [Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration](https://mlsanity.com/paper/1902.05454/) (2019)
- [On Performance Estimation in Automatic Algorithm Configuration](https://mlsanity.com/paper/1911.08200/) (2019)
- [Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis](https://mlsanity.com/paper/2007.02816/) (2020)
- [Bayesian Optimization With Censored Response Data](https://mlsanity.com/paper/1310.1947/) (2013)
- [Neural Model-based Optimization with Right-Censored Observations](https://mlsanity.com/paper/2009.13828/) (2020)
- [Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees](https://mlsanity.com/paper/1905.10819/) (2019)
- [Preselection Bandits](https://mlsanity.com/paper/1907.06123/) (2019)

## Built on by (most cited first, 30 of 30)

- [Hyperparameters in Reinforcement Learning and How To Tune Them](https://mlsanity.com/paper/2306.01324/) (2023)
- [AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks](https://mlsanity.com/paper/2306.08107/) (2023)
- [Automated Design of Metaheuristic Algorithms: A Survey](https://mlsanity.com/paper/2303.06532/) (2023)
- [Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process](https://mlsanity.com/paper/2401.03244/) (2024)
- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems](https://mlsanity.com/paper/2401.01192/) (2024)
- [Automated Metaheuristic Algorithm Design with Autoregressive Learning](https://mlsanity.com/paper/2405.03419/) (2024)
- [AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration](https://mlsanity.com/paper/2212.00333/) (2022)
- [Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget](https://mlsanity.com/paper/2202.04487/) (2022)
- [Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize](https://mlsanity.com/paper/2310.07082/) (2023)
- [Accelerating process control and optimization via machine learning: A review](https://mlsanity.com/paper/2412.18529/) (2024)
- [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)
- [ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning](https://mlsanity.com/paper/2409.18827/) (2024)
- [Regularization in Spider-Style Strategy Discovery and Schedule Construction](https://mlsanity.com/paper/2403.12869/) (2024)
- [Graph-Based Imitation and Reinforcement Learning for Efficient Benders Decomposition](https://mlsanity.com/paper/2511.11870/) (2025)
- [Scalable Dynamic Distributed Constraint Optimization with Metareasoning and Application to Continual Satellite Operations](https://mlsanity.com/paper/2601.06188/) (2026)
- [From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors](https://mlsanity.com/paper/2603.02792/) (2026)
- [UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learning](https://mlsanity.com/paper/2210.14030/) (2022)
- [AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design](https://mlsanity.com/paper/2303.06536/) (2023)
- [Discovering Multiple Algorithm Configurations](https://mlsanity.com/paper/2303.07434/) (2023)
- [LEO: Learning Efficient Orderings for Multiobjective Binary Decision Diagrams](https://mlsanity.com/paper/2307.03171/) (2023)
- [Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction](https://mlsanity.com/paper/2506.16144/) (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)
- [GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs](https://mlsanity.com/paper/2606.23299/) (2026)
- [Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets](https://mlsanity.com/paper/2409.08636/) (2024)
- [A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs](https://mlsanity.com/paper/2506.04238/) (2025)
- [A Review on Single-Problem Multi-Attempt Heuristic Optimization](https://mlsanity.com/paper/2509.26321/) (2025)
- [Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network](https://mlsanity.com/paper/2512.16491/) (2025)
- [Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization](https://mlsanity.com/paper/2606.04866/) (2026)
- [DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance](https://mlsanity.com/paper/2609.36472/) (2026)

## 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.