Literature Overview

Maintained by Steven Adriaensen and André Biedenkapp.

The following list considers papers related to dynamic algorithm configuration. It is by no means complete. If you miss a paper on the list, please let us know.

Please note that dynamic configuration has been studied in many different communities (under many different names) and each community has developed a slightly different focus or evaluation criteria. Our criteria for maintaining this literature list are as follows:

  • Does the presented work change (hyper-)parameters on the fly (i.e., during the run of a target algorithm)?
  • Is this done in an automated fashion (e.g., via a learned update policy)?
  • Does it have a meta-learning component (i.e., can the configuration policies be transferred to problems that it has not been ‘learned’ on)?



Ichnowski, Jeffrey; Jain, Paras; Stellato, Bartolomeo; Banjac, Goran; Luo, Michael; Borrelli, Francesco; Gonzalez, Joseph E.; Stoica, Ion; Goldberg, Ken

Accelerating Quadratic Optimization with Reinforcement Learning Unpublished


Abstract | Links | BibTeX


Bhatia, Abhinav; Svegliato, Justin; Zilberstein, Shlomo

Tuning the Hyperparameters of Anytime Planning: A Deep Reinforcement Learning Approach Inproceedings

In: ICAPS 2021 Workshop on Heuristics and Search for Domain-independent Planning, 2021.

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Almeida, Diogo; Winter, Clemens; Tang, Jie; Zaremba, Wojciech

A Generalizable Approach to Learning Optimizers Unpublished


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Speck, D; Biedenkapp, A; Hutter, F; Mattmüller, R; Lindauer, M

Learning Heuristic Selection with Dynamic Algorithm Configuration Inproceedings

In: Zhuo, H H; Yang, Q; Do, M; Goldman, R; Biundo, S; Katz, M (Ed.): Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS'21), pp. 597–605, AAAI, 2021.

Links | BibTeX


Eimer, T; Biedenkapp, A; Reimer, M; Adriaensen, S; Hutter, F; Lindauer, M

DACBench: A Benchmark Library for Dynamic Algorithm Configuration Inproceedings

In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI'21),, 2021.

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Biedenkapp, André; Bozkurt, H. Furkan; Eimer, Theresa; Hutter, Frank; Lindauer, Marius

Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework Conference

Proceedings of the Twenty-fourth European Conference on Artificial Intelligence (ECAI'20), 2020.

Abstract | Links | BibTeX


Gomoluch, Pawel; Alrajeh, Dalal; Russo, Alessandra; Bucchiarone, Antonio

Learning Neural Search Policies for Classical Planning Inproceedings

In: Proceedings of the International Conference on Automated Planning and Scheduling, pp. 522–530, 2020.

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Shala, G; Biedenkapp, A; Awad, N; Adriaensen, S; Lindauer, M; Hutter, F

Learning Step-Size Adaptation in CMA-ES Inproceedings

In: Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN'20), pp. 691–706, Springer, 2020.

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Sae-Dan, Weerapan; Kessaci, Marie-Eléonore; Veerapen, Nadarajen; Jourdan, Laetitia

Time-Dependent Automatic Parameter Configuration of a Local Search Algorithm Inproceedings

In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1898–1905, Association for Computing Machinery, Cancún, Mexico, 2020, ISBN: 9781450371278.

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Xu, Z; Dai, A M; Kemp, J; Metz, L

Learning an Adaptive Learning Rate Schedule Unpublished

2019, (textitarXiv:1909.09712 [cs.LG]).

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Sharma, Mudita; Komninos, Alexandros; nez, Manuel López-Ibá; Kazakov, Dimitar

Deep reinforcement learning based parameter control in differential evolution Inproceedings

In: Auger, A; ü, St T (Ed.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'19), pp. 709–717, ACM, 2019.

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Gomoluch, Paweł; Alrajeh, Dalal; Russo, Alessandra

Learning classical planning strategies with policy gradient Inproceedings

In: Proceedings of the International Conference on Automated Planning and Scheduling, pp. 637–645, 2019.

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Ansótegui, Carlos; Pon, Josep; Sellmann, Meinolf; Tierney, Kevin

Reactive Dialectic Search Portfolios for MaxSAT Inproceedings

In: S.Singh,; Markovitch, S (Ed.): Proceedings of the Conference on Artificial Intelligence (AAAI'17), pp. 765–772, AAAI Press, 2017.

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Xu, Chang; Qin, Tao; Wang, Gang; Liu, Tie-Yan

Reinforcement learning for learning rate control Journal Article

In: arXiv preprint arXiv:1705.11159, 2017.

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Kadioglu, S; Sellmann, M; Wagner, M

Learning a reactive restart strategy to improve stochastic search Inproceedings

In: International Conference on Learning and Intelligent Optimization, pp. 109–123, Springer 2017.

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Adriaensen, S; Nowé, A

Towards a White Box Approach to Automated Algorithm Design Inproceedings

In: Kambhampati, S (Ed.): Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'16), pp. 554–560, 2016.

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Daniel, C; Taylor, J; Nowozin, S

Learning Step Size Controllers for Robust Neural Network Training Inproceedings

In: Schuurmans, D; Wellman, M (Ed.): Proceedings of the Thirtieth National Conference on Artificial Intelligence (AAAI'16), AAAI Press, 2016.

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Andersson, Martin; Bandaru, Sunith; Ng, Amos HC

Tuning of Multiple Parameter Sets in Evolutionary Algorithms Inproceedings

In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 533–540, 2016.

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Hansen, Samantha

Using Deep Q-Learning to Control Optimization Hyperparameters Journal Article

In: arXiv preprint arXiv:1602.04062, 2016.

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López-Ibánez, Manuel; Stützle, Thomas

Automatically Improving the Anytime Behaviour of Optimisation Algorithms Journal Article

In: European Journal of Operational Research, 235 (3), pp. 569–582, 2014.

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Battiti, R; Campigotto, P

An Investigation of Reinforcement Learning for Reactive Search Optimization Incollection

In: Hamadi, Y; Monfroy, E; Saubion, F (Ed.): Autonomous Search, pp. 131–160, Springer, 2012.

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Xu, Yuehua; Fern, Alan; Yoon, Sungwook

Iterative Learning of Weighted Rule Sets for Greedy Search Conference

Proceedings of the 20th International Conference on Automated Planning and Scheduling (ICAPS'10), 2010.

Abstract | Links | BibTeX


Sakurai, Y; Takada, K; Kawabe, T; Tsuruta, S

A Method to Control Parameters of Evolutionary Algorithms by Using Reinforcement Learning Inproceedings

In: é, K Y; Dipanda, A; Chbeir, R (Ed.): Proceedings of Sixth International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp. 74–79, IEEE Computer Society, 2010.

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Fialho, Alvaro; Costa, Luis Da; Schoenauer, Marc; Sebag, Michele

Analyzing Bandit-Based Adaptive Operator Selection Mechanisms Journal Article

In: Annals of Mathematics and Artificial Intelligence, 60 (1), pp. 25–64, 2010.

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Pettinger, J; Everson, R

Controlling Genetic Algorithms with Reinforcement Learning Inproceedings

In: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, pp. 692–692, 2002.

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Lagoudakis, M; Littman, M

Learning to Select Branching Rules in the DPLL Procedure for Satisfiability Journal Article

In: Electronic Notes in Discrete Mathematics, 9 , pp. 344–359, 2001.

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Lagoudakis, Michail G.; Littman, Michael L.

Algorithm Selection using Reinforcement Learning Conference

Proceedings of the 17th International Conference on Machine Learning (ICML 2000), 2000.

Abstract | Links | BibTeX