cmaes: A Simple yet Practical Python Library for CMA-ES

Understand

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.