A Python Benchmark Functions Framework for Numerical Optimisation Problems
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910499584606208 |
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| author | Baronti, Luca Castellani, Marco |
| author_facet | Baronti, Luca Castellani, Marco |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16195 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Python Benchmark Functions Framework for Numerical Optimisation Problems Baronti, Luca Castellani, Marco Numerical Analysis Optimization and Control 49, 68 I.2.8; G.1 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 |
| title | A Python Benchmark Functions Framework for Numerical Optimisation Problems |
| topic | Numerical Analysis Optimization and Control 49, 68 I.2.8; G.1 |
| url | https://arxiv.org/abs/2406.16195 |