cmaes : A Simple yet Practical Python Library for CMA-ES
<|reference_start|>cmaes : A Simple yet Practical Python Library for CMA-ES: 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 yet potent 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 quickly using CMA-ES, as well as for educational purposes and seamless integration into other libraries. Despite its simplistic design, cmaes maintains enhanced functionality. It incorporates recent advancements in CMA-ES, such as learning rate adaptation for challenging scenarios, transfer learning, and mixed-integer optimization capabilities. These advanced features are accessible through a user-friendly API, ensuring that cmaes can be easily adopted in practical applications. We regard cmaes as the first choice for a Python CMA-ES library among practitioners. The software is available under the MIT license at https://github.com/CyberAgentAILab/cmaes.<|reference_end|>
@article{nomura2024cmaes,
    title={cmaes : A Simple yet Practical Python Library for CMA-ES},
    author={Masahiro Nomura, Masashi Shibata},
    journal={arXiv preprint arXiv:2402.01373},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01373},
    primaryClass={cs.NE cs.MS}
}