bbob-jax 1.4.0

BBOB Benchmark function implemented in JAX

BBOB Benchmark set for JAX

| GitHub | PyPI | Documentation | Zenodo

JAX implementation of the BBOB Benchmark functions for black-box optimization, based on the original definitions by Finck et al. (2009) [^1].

First publication: October 17, 2025

---

Statement of need

This repository provides the original BBOB 24 noise-free, real-parameter, single-objective benchmark functions reimplemented in JAX. Originally written in C, these functions have been translated to JAX to enable automatic differentiation, just-in-time (JIT) compilation, and XLA-accelerated performance; making them ideal for research in optimization, machine learning, and evolutionary algorithms.

3D surface plots of the 24 BBOB benchmark functions.


2D contour plots of the 24 BBOB benchmark functions.

Authorship & Citation

Authors:

• Martin van der Schelling ([e-mail])

Authors affiliation:

• Delft University of Technology (Bessa Research Group)

Maintainer:

• Martin van der Schelling ([e-mail])

Maintainer affiliation:

• Delft University of Technology (Bessa Research Group)

If you use bbob-jax in your research or in a scientific publication, it is appreciated that you cite the paper below:

Zenodo (link):

@software{vanderSchelling2025,
  title        = {Black-box optimization benchmarking (bbob) problem
                   set for JAX},
  author       = {van der Schelling, M. P. and Bessa, M A.},
  month        = {nov},
  year         = {2025},
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.17426894},
  url          = {https://doi.org/10.5281/zenodo.17426894},
}

Getting started

To install the package, use pip:

pip install bbob-jax

Related Work

This project builds on and complements established benchmarking efforts and tooling in black-box optimization. The resources below are closely related and provide broader context and utilities.

• COCO platform (COmparing Continuous Optimisers): benchmarking framework and tools for black-box optimization. [^2]
• EvoSax: JAX-based evolution strategies library that includes BBOB function support and benchmarking utilities. [^3]

Community Support

If you find any issues, bugs or problems with this package, please use the GitHub issue tracker to report them.

License

Copyright (c) 2025, Martin van der Schelling

All rights reserved.

This project is licensed under the BSD 3-Clause License. See LICENSE for the full license text.

[^1]: Finck, S., Hansen, N., Ros, R., and Auger, A. (2009), Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions, INRIA.

[^2]: Hansen, N., Auger, A., Ros, R., Mersmann, O., Tušar, T., and Brockhoff, D. (2021), COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting. Optimization Methods and Software, 36(1), 114–144. https://doi.org/10.1080/10556788.2020.1808977

[^3]: Lange, R. T. (2022), evosax: JAX-based Evolution Strategies. arXiv preprint arXiv:2212.04180.