The Algorithm Selection Competitions 2015 and 2017
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Abstract (arXiv)
The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection competitions in 2015 and 2017. The results of these competitions show how the state of the art improved over the years. We show that although performance in some cases is very good, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 2; 2025: 0; 2026: 3.
12 papers.
- 2021Algorithm Selection on a Meta Levelcited by 11
- 2022Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classificationcited by 10
- 2023CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failurecited by 8
- 2023Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoMLcited by 8
- 2022Advances in MetaDL: AAAI 2021 challenge and workshopcited by 4
- 2022On the evaluation of (meta-)solver approachescited by 0
- 2023Automatic Algorithm Selection for Pseudo-Boolean Optimization with Given Computational Time Limitscited by 0
- 2024Characterising harmful data sources when constructing multi-fidelity surrogate modelscited by 0
- 2024On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Settingcited by 0
- 2026Algorithm Selection with Zero Domain Knowledge via Text Embeddingscited by 0
- 2026Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selectioncited by 0
- 2026Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluationscited by 0
- 2026Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations10 Oct 2026
- 2026Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selection12 Sep 2026
- 2026Algorithm Selection with Zero Domain Knowledge via Text Embeddings20 Mar 2026
- 2024On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting13 May 2024
- 2024Characterising harmful data sources when constructing multi-fidelity surrogate models12 Mar 2024
- 2023Automatic Algorithm Selection for Pseudo-Boolean Optimization with Given Computational Time Limits7 Sep 2023
- 2023Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML17 Jul 2023
- 2023CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure1 Jul 2023
- 2022Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification15 Jun 2022
- 2022On the evaluation of (meta-)solver approaches17 Feb 2022
- 2022Advances in MetaDL: AAAI 2021 challenge and workshop1 Feb 2022
- 2021Algorithm Selection on a Meta Level20 Jul 2021