Implementation in Python of evolutionary algorithms, used to make an study of the performance in Python using Cython. This code is GPLv3.

You can read a post about that in http://dmolina.github.com/blog/2012/07/15/increasing-performance-python/, this is the source code used in this comparisons.

To install, you need Python 2.6, [Boost::python] (http://www.boost.org/doc/libs/1_50_0/libs/python/doc/ "Boost::Python"), and [CMake] (http://cmake.org/ "CMake") to compile.

The install process has several steps, but you can only run the ./compile script:

./compile

and everything should be compiled.

You can use the algorithm in the following way:

Usage: runcec.py [options]

Options: -h, --help show this help message and exit -f FUNCTION, --function=FUNCTION set the function to optimise (number between [1,25]) -d FUNCTION, --dimension=FUNCTION set the dimensionality (2|10|30|50) -t FUNCTION, --times=FUNCTION set the run number (default=25)

Example: python runcec.py -f 1 -d 10 -t 5

By default, the algorithm uses the cython version. If you want to repeat the experiments of the post, or you want to study the differences, these methods are implemented in two modules:

• utils.py : performance critic methods using python.
• cutils.py: performance critic method using cython.

To set the the slow (utils.py) or fast (cutils.py) you only have to comment (or uncomment) one of the following lines:

from cutils import getParentByNAM,crossBLX,getBestWorst #uncomment for fast version (cython) #from utils import getParentByNAM,crossBLX,getBestWorst #uncomment for slow version (python)