pycma

A Python implementation of CMA-ES and a few related numerical optimization tools.

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic derivative-free numerical optimization algorithm for difficult (non-convex, ill-conditioned, multi-modal, rugged, noisy) optimization problems in continuous search spaces.

Useful links:

• A quick start guide with a few usage examples

• The API Documentation

• Hints for how to use this (kind of) optimization module in practice

Installation of the latest release

Type

  python -m pip install cma

in a system shell to install the latest release from the Python Package Index (PyPI). The release link also provides more installation hints and a quick start guide.

Installation of the current master branch

Download and unzip the code (see green button above) or git clone https://github.com/CMA-ES/pycma.git.

• Either, copy (or move) the cma source code folder into a folder visible to Python, namely a folder which is in the Python path (e.g. the current folder). Then, import cma works without any further installation.

• Or, install the cma package by typing within the folder, where the cma source code folder is visible,

  python -m pip install -e cma
  

  Typing pip instead of python -m pip may be sufficient, prefixing with sudo may be necessary. Moving the cma folder away from this location would invalidate the installation.

Version History

• Version 2.2.0 added VkD CMA-ES to the master branch.

• Version 2.* is a multi-file split-up of the original module.

• Version 1.x.* is a one file implementation and not available in the history of this repository. The latest 1.* version ```1.1.7`` can be found here.