CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical
optimization in Python

a stochastic numerical optimization algorithm for difficult (non-convex,
ill-conditioned, multimodal) optimization problems in continuous search
spaces, implemented in Python.

Typical domain of application are objective functions with:

 search space dimension between 5 and 100,
 at least about 100 times dimension function evaluations needed to get
 satisfactory solutions, non-separable, ill-conditioned, or rugged/multi-modal
 landscapes