  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.
