cmaes: A Simple yet Practical Python Library for CMA-ES
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908818565234688 |
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| author | Nomura, Masahiro Shibata, Masashi Hamano, Ryoki |
| author_facet | Nomura, Masahiro Shibata, Masashi Hamano, Ryoki |
| contents | The covariance matrix adaptation evolution strategy (CMA-ES) has been highly effective in black-box continuous optimization, as demonstrated by its success in both benchmark problems and various real-world applications. To address the need for an accessible and powerful tool in this domain, we developed cmaes, a simple and practical Python library for CMA-ES. cmaes is characterized by its simplicity, offering intuitive use and high code readability. This makes it suitable for quick use of CMA-ES, as well as for educational purposes and seamless integration into other libraries. Despite its simple design, cmaes maintains advanced functionality. It incorporates recent advancements in CMA-ES, such as learning rate adaptation for challenging scenarios, transfer learning, mixed-variable optimization, and multi-objective optimization capabilities. These advanced features are accessible through a user-friendly API, ensuring that cmaes can be easily adopted in practical applications. We present cmaes as a strong candidate for a practical Python CMA-ES library aimed at practitioners. The software is available under the MIT license at https://github.com/CyberAgentAILab/cmaes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01373 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | cmaes: A Simple yet Practical Python Library for CMA-ES Nomura, Masahiro Shibata, Masashi Hamano, Ryoki Neural and Evolutionary Computing Mathematical Software The covariance matrix adaptation evolution strategy (CMA-ES) has been highly effective in black-box continuous optimization, as demonstrated by its success in both benchmark problems and various real-world applications. To address the need for an accessible and powerful tool in this domain, we developed cmaes, a simple and practical Python library for CMA-ES. cmaes is characterized by its simplicity, offering intuitive use and high code readability. This makes it suitable for quick use of CMA-ES, as well as for educational purposes and seamless integration into other libraries. Despite its simple design, cmaes maintains advanced functionality. It incorporates recent advancements in CMA-ES, such as learning rate adaptation for challenging scenarios, transfer learning, mixed-variable optimization, and multi-objective optimization capabilities. These advanced features are accessible through a user-friendly API, ensuring that cmaes can be easily adopted in practical applications. We present cmaes as a strong candidate for a practical Python CMA-ES library aimed at practitioners. The software is available under the MIT license at https://github.com/CyberAgentAILab/cmaes. |
| title | cmaes: A Simple yet Practical Python Library for CMA-ES |
| topic | Neural and Evolutionary Computing Mathematical Software |
| url | https://arxiv.org/abs/2402.01373 |