Papers › A Python Benchmark Functions Framework for Numerical Optimisation Problems
A Python Benchmark Functions Framework for Numerical Optimisation Problems
Luca Baronti, Marco Castellani
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
This work proposes a framework of benchmark functions designed to facilitate the creation of test cases for numerical optimisation techniques. The framework, written in Python 3, is designed to be easy to install, use, and expand. The collection includes some of the most used multi-modal continuous functions present in literature, which can be instantiated using an arbitrary number of dimensions. Meta-information of each benchmark function, like search boundaries and position of known optima, are included and made easily accessible through class methods. Built-in interactive visualisation capabilities, baseline techniques, and rigorous testing protocols complement the features of the framework. The framework can be found here: \url{https://gitlab.com/luca.baronti/python_benchmark_functions
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections