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.
python -m pip install cma
Installation of the current master branch
The quick way (requires git to be installed):
pip install git+https://github.com/CMA-ES/pycma.git@master
The long version: download and unzip the code (see green button above) or
git clone https://github.com/CMA-ES/pycma.git.
Either, copy (or move) the
cmasource code folder into a folder visible to Python, namely a folder which is in the Python path (e.g. the current folder). Then,
import cmaworks without any further installation.
Or, install the
cmapackage by typing within the folder, where the
cmasource code folder is visible,
pip install -e cma
cmafolder away from its location would invalidate this installation.
It may be necessary to replace
python -m pip and/or prefixing
either of these with
2.4.2added the function
cma.fmin2which, similar to
(x_best:numpy.ndarray, es:cma.CMAEvolutionStrategy)instead of a 10-tuple like
2.2.0added VkD CMA-ES to the master branch.
2.*is a multi-file split-up of the original module.
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.