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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)?


2022

35.

Michele Tessari, Giovanni Iacca

Reinforcement learning based adaptive metaheuristics Workshop

Genetic and Evolutionary Computation Conference (GECCO) 2022, Companion Proceedings, 2022.

Abstract | Links | BibTeX

34.

Biedenkapp, André; Dang, Nguyen; Krejca, Martin S.; Hutter, Frank; Doerr, Carola

Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration Inproceedings

In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22), 2022.

Abstract | Links | BibTeX

33.

Biedenkapp, André; Speck, David; Sievers, Silvan; Hutter, Frank; Lindauer, Marius; Seipp, Jendrik

Learning Domain-Independent Policies for Open List Selection Workshop

Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL @ ICAPS'22), 2022.

Abstract | Links | BibTeX

32.

Bhatia, Abhinav; Svegliato, Justin; Nashed, Samer B.; Zilberstein, Shlomo

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

In: Proceedings of the 32nd International Conference on Automated Planning and Scheduling (ICAPS'22), 2022.

Abstract | Links | BibTeX

31.

Adriaensen, Steven; Biedenkapp, André; Shala, Gresa; Awad, Noor; Eimer, Theresa; Lindauer, Marius; Hutter, Frank

Automated Dynamic Algorithm Configuration Journal Article

In: arXiv:2205.13881, 2022.

Abstract | Links | BibTeX

2021

30.

Getzelman, Grant; Balaprakash, Prasanna

Learning to Switch Optimizers for Quadratic Programming Inproceedings

In: Balasubramanian, Vineeth N.; Tsang, Ivor (Ed.): Proceedings of The 13th Asian Conference on Machine Learning, pp. 1553–1568, PMLR, 2021.

Abstract | Links | BibTeX

29.

Olegovich Malashin, Roman

Sparsely Ensembled Convolutional Neural Network Classifiers via Reinforcement Learning Inproceedings

In: 2021 6th International Conference on Machine Learning Technologies, pp. 102–110, 2021, ISBN: 9781450389402.

Abstract | Links | BibTeX

28.

Nguyen, Manh Hung; Grinsztajn, Nathan; Guyon, Isabelle; Sun-Hosoy, Lisheng

MetaREVEAL: RL-based Meta-learning from Learning Curves Inproceedings

In: Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), 2021.

Abstract | Links | BibTeX

27.

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

2021.

Abstract | Links | BibTeX

26.

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

25.

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), ijcai.org, 2021.

Links | BibTeX

24.

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

A Generalizable Approach to Learning Optimizers Unpublished

2021.

Links | BibTeX

23.

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.

Links | BibTeX

2020

22.

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

21.

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.

Links | BibTeX

20.

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.

Links | BibTeX

19.

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.

Links | BibTeX

2019

18.

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

Learning an Adaptive Learning Rate Schedule Unpublished

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

Links | BibTeX

17.

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.

Links | BibTeX

16.

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.

Links | BibTeX

2017

15.

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.

Links | BibTeX

14.

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

Reinforcement learning for learning rate control Journal Article

In: arXiv preprint arXiv:1705.11159, 2017.

Links | BibTeX

13.

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.

Links | BibTeX

2016

12.

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.

Links | BibTeX

11.

Hansen, Samantha

Using Deep Q-Learning to Control Optimization Hyperparameters Journal Article

In: arXiv preprint arXiv:1602.04062, 2016.

Links | BibTeX

10.

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.

Links | BibTeX

9.

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.

Links | BibTeX

2014

8.

López-Ibánez, Manuel; Stützle, Thomas

Automatically Improving the Anytime Behaviour of Optimisation Algorithms Journal Article

In: European Journal of Operational Research, vol. 235, no. 3, pp. 569–582, 2014.

Links | BibTeX

2012

7.

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.

Links | BibTeX

2010

6.

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

5.

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.

Links | BibTeX

4.

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, vol. 60, no. 1, pp. 25–64, 2010.

Links | BibTeX

2002

3.

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.

Links | BibTeX

2001

2.

Lagoudakis, M; Littman, M

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

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

Links | BibTeX

2000

1.

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