./wip/py-cma, Covariance Matrix Adaptation Evolution Strategy for non-linear

[ CVSweb ] [ Homepage ] [ RSS ] [ Required by ] [ Add to tracker ]

Branch: CURRENT, Version: 1.1.01, Package name: py310-cma-1.1.01, Maintainer: jihbed.research

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


Required to run:
[devel/py-setuptools] [math/py-numpy] [lang/python37]

Required to build:
[pkgtools/cwrappers]

Master sites:

RMD160: cd7f045298a8aefc4ba922dfdbd1bba87746aeb0
Filesize: 98.004 KB

Version history: (Expand)

CVS history: (Expand)


   2014-08-22 23:12:24 by Kamel Derouiche | Files touched by this commit (3) | Package updated
Log message:

	New version
	update dependency: numpy
	fix to python installation method
   2014-05-05 00:27:47 by Kamel Derouiche | Files touched by this commit (4) 
Log message:
Import py27-cma-1.0.02beta as wip/py-cma.

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