DEAP release 1.0.0: Distributed Evolutionary Algorithms in Python

[Félix-Antoine Fortin]

After more than 4 years of development, we are proud to announce the 
release of DEAP 1.0.0. You can download a copy of this release at the 
following web page. 

https://pypi.python.org/pypi/deap

DEAP (Distributed Evolutionary Algorithms in Python) is a novel evolutionary 
computation framework for rapid prototyping and testing of ideas. Its design 
departs from most other existing frameworks in that it seeks to make algorithms 
explicit and data structures transparent, as opposed to the more common black 
box type of frameworks.

To get to know more about DEAP and the current release, we invite you
to read the most recent article on DEAP published in SIGEvolution volume 6, 
issue 2, pp. 17-26.

http://sigevolution.org/

An IPython notebook version of the article is also available.

https://github.com/DEAP/notebooks

This release includes:
    - Major overhaul of statistics computing and logging;
    - Ability to do Object Oriented Genetic Programming (OOGP);
    - Symbolic regression benchmarks for GP;
    - New tutorials and better documentation;
    - Several new examples from diverse fields;
    - and several other changes.

Every changes of this release are detailed in the documentation.

http://deap.gel.ulaval.ca/doc/default/releases.html

To help users translate code from 0.9.x to 1.0.0, we have also written
a new porting guide that details every change required to use DEAP 1.0.

http://deap.gel.ulaval.ca/doc/default/porting.html

Your feedback and comments are welcome at http://goo.gl/LZkdi4 or 
deap-users at googlegroups dot com. You can also follow us on Twitter @deapdev, 
and on our blog http://deapdev.wordpress.com/.

Best,

François-Michel De Rainville
Félix-Antoine Fortin
Marc-André Gardner
Christian Gagné
Marc Parizeau

Laboratoire de vision et systèmes numériques
Département de génie électrique et génie informatique
Université Laval
Quebec City (Quebec), Canada