A Python Library for Classical, Limited-Memory, and Dynamical Low-Rank Covariance Matrix Adaptation Evolution Strategy

General :earth_americas:

seamaze is a Python library for classical, limited-memory, and dynamical low-rank (DLR) variants of the covariance matrix adaptation evolution strategy (CMA-ES). It provides state-of-the-art, derivative-free algorithms designed for continuous, non-linear, and non-convex real-parameter optimization, excelling in ill-conditioned, non-separable, or rugged fitness landscapes. By leveraging limited-memory and DLR approximations, seamaze maintains computational efficiency even on high-dimensional black-box problems. This implementation further incorporates first-order information, constraint handling, and multi-stage restart mechanisms.

Installation :computer:

Python distribution

You can install the latest distribution via:

pip install seamaze

Source code

You can check the latest source code via:

git clone https://github.com/pyanno4rt/seamaze.git

Usage

seamaze has three main classes which provide a classical, a limited-memory, and a dynamical low-rank CMA-ES variant. Check out the 'examples' folder for detailed benchmark scripts!

Classical CMA-ES

from seamaze.optimizers import CMAES

Limited-memory CMA-ES

from seamaze.optimizers import LMMAES

Dynamical low-rank CMA-ES

from seamaze.optimizers import DLRCMAES

Dependencies

Development :rocket:

Important links

• Github
• PyPI
• Coveralls
• Issue tracker

Help and Support :busts_in_silhouette:

Resources

• Github Discussions
• Github Issues

Contact

• Github Page
• Mail
• LinkedIn

Citation

To cite seamaze, either use the link in the right sidebar of the Github landing page labeled "Cite this repository" or copy the short-form bib-style paragraph below:

@software{seamaze,
  title = {{seamaze}: a python library for classical, limited-memory, and dynamical low-rank covariance matrix adaptation evolution strategy},
  author = {Ortkamp, Tim and Patwardhan, Chinmay and Stammer, Pia},
  version = {0.0.10},
  license = {MIT},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/pyanno4rt/seamaze}
}