# COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite

arXiv: https://arxiv.org/abs/1903.06396 · HTML: https://mlsanity.com/paper/1903.06396/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 12 of 12)

- [Towards Learning Universal Hyperparameter Optimizers with Transformers](https://mlsanity.com/paper/2205.13320/) (2022)
- [OmniPred: Language Models as Universal Regressors](https://mlsanity.com/paper/2402.14547/) (2024)
- [Position: Leverage Foundational Models for Black-Box Optimization](https://mlsanity.com/paper/2405.03547/) (2024)
- [Understanding LLM Embeddings for Regression](https://mlsanity.com/paper/2411.14708/) (2024)
- [Reinforced In-Context Black-Box Optimization](https://mlsanity.com/paper/2402.17423/) (2024)
- [The Vizier Gaussian Process Bandit Algorithm](https://mlsanity.com/paper/2408.11527/) (2024)
- [Decoding-based Regression](https://mlsanity.com/paper/2501.19383/) (2025)
- [Language Model Embeddings Can Be Sufficient for Bayesian Optimization](https://mlsanity.com/paper/2410.10190/) (2024)
- [Multi-step Planning for Automated Hyperparameter Optimization with OptFormer](https://mlsanity.com/paper/2210.04971/) (2022)
- [ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization](https://mlsanity.com/paper/2510.03051/) (2025)
- [Island-Based Evolutionary Computation with Diverse Surrogates and Adaptive Knowledge Transfer for High-Dimensional Data-Driven Optimization](https://mlsanity.com/paper/2503.12856/) (2025)
- [Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks](https://mlsanity.com/paper/2608.13296/) (2026)

## 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.