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)?
In: ICAPS 2021 Workshop on Heuristics and Search for Domain-independent Planning, 2021.
A Generalizable Approach to Learning Optimizers Unpublished
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.
In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI'21), ijcai.org, 2021.
Proceedings of the Twenty-fourth European Conference on Artificial Intelligence (ECAI'20), 2020.
Learning Neural Search Policies for Classical Planning Inproceedings
In: Proceedings of the International Conference on Automated Planning and Scheduling, pp. 522–530, 2020.
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.
In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1898–1905, Association for Computing Machinery, Cancún, Mexico, 2020, ISBN: 9781450371278.
Learning an Adaptive Learning Rate Schedule Unpublished
2019, (textitarXiv:1909.09712 [cs.LG]).
In: Auger, A; ü, St T (Ed.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'19), pp. 709–717, ACM, 2019.
In: Proceedings of the International Conference on Automated Planning and Scheduling, pp. 637–645, 2019.
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.
Reinforcement learning for learning rate control Journal Article
In: arXiv preprint arXiv:1705.11159, 2017.
In: International Conference on Learning and Intelligent Optimization, pp. 109–123, Springer 2017.
In: Kambhampati, S (Ed.): Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'16), pp. 554–560, 2016.
In: Schuurmans, D; Wellman, M (Ed.): Proceedings of the Thirtieth National Conference on Artificial Intelligence (AAAI'16), AAAI Press, 2016.
In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 533–540, 2016.
Using Deep Q-Learning to Control Optimization Hyperparameters Journal Article
In: arXiv preprint arXiv:1602.04062, 2016.
In: European Journal of Operational Research, 235 (3), pp. 569–582, 2014.
In: Hamadi, Y; Monfroy, E; Saubion, F (Ed.): Autonomous Search, pp. 131–160, Springer, 2012.
Proceedings of the 20th International Conference on Automated Planning and Scheduling (ICAPS'10), 2010.
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.
Analyzing Bandit-Based Adaptive Operator Selection Mechanisms Journal Article
In: Annals of Mathematics and Artificial Intelligence, 60 (1), pp. 25–64, 2010.
In: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, pp. 692–692, 2002.
In: Electronic Notes in Discrete Mathematics, 9 , pp. 344–359, 2001.
Proceedings of the 17th International Conference on Machine Learning (ICML 2000), 2000.