COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite
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Abstract (arXiv)
The bbob-largescale test suite, containing 24 single-objective functions in continuous domain, extends the well-known single-objective noiseless bbob test suite, which has been used since 2009 in the BBOB workshop series, to large dimension. The core idea is to make the rotational transformations R, Q in search space that appear in the bbob test suite computationally cheaper while retaining some desired properties. This documentation presents an approach that replaces a full rotational transformation with a combination of a block-diagonal matrix and two permutation matrices in order to construct test functions whose computational and memory costs scale linearly in the dimension of the problem.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2019.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 6; 2025: 3; 2026: 1.
12 papers.
- 2022Towards Learning Universal Hyperparameter Optimizers with Transformerscited by 81
- 2024OmniPred: Language Models as Universal Regressorscited by 34
- 2024Position: Leverage Foundational Models for Black-Box Optimizationcited by 21
- 2024Understanding LLM Embeddings for Regressioncited by 19
- 2024Reinforced In-Context Black-Box Optimizationcited by 14
- 2024The Vizier Gaussian Process Bandit Algorithmcited by 14
- 2025Decoding-based Regressioncited by 11
- 2024Language Model Embeddings Can Be Sufficient for Bayesian Optimizationcited by 8
- 2022Multi-step Planning for Automated Hyperparameter Optimization with OptFormercited by 2
- 2025ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimizationcited by 1
- 2025Island-Based Evolutionary Computation with Diverse Surrogates and Adaptive Knowledge Transfer for High-Dimensional Data-Driven Optimizationcited by 0
- 2026Large-scale Testing Global Optimization Methods with Black-box Adversarial Attackscited by 0
- 2026Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks13 Aug 2026
- 2025ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization3 Oct 2025
- 2025Island-Based Evolutionary Computation with Diverse Surrogates and Adaptive Knowledge Transfer for High-Dimensional Data-Driven Optimization17 Mar 2025
- 2025Decoding-based Regression31 Jan 2025
- 2024Understanding LLM Embeddings for Regression22 Nov 2024
- 2024Language Model Embeddings Can Be Sufficient for Bayesian Optimization14 Oct 2024
- 2024The Vizier Gaussian Process Bandit Algorithm21 Aug 2024
- 2024Position: Leverage Foundational Models for Black-Box Optimization6 May 2024
- 2024Reinforced In-Context Black-Box Optimization27 Feb 2024
- 2024OmniPred: Language Models as Universal Regressors22 Feb 2024
- 2022Multi-step Planning for Automated Hyperparameter Optimization with OptFormer10 Oct 2022
- 2022Towards Learning Universal Hyperparameter Optimizers with Transformers26 May 2022