DEAP 0.8 released - Distributed Evolutionary Algorithms in Python

[Félix-Antoine Fortin]

Hi everyone, 

We are proud to annouce the release of DEAP 0.8, a library for doing 
Distributed Evolutionary Algorithms in Python. You can download a copy 
of this release at the following web page. 

http://deap.googlecode.com 

This release includes :
	- compatibility with Python 3;
	- a new algorithm : generate-update
	- a lot of new examples;
	- a lot of new benchmarks;
	- History can now return the genealogy of a single individual;
	- C++ version of the NSGA-2 algorithm	
	- more detailed documentation with new tutorials and examples;
	- new theme for the documentation;
	- and many more.

Users of DEAP 0.7 should be aware that some of the modifications
included with 0.8 will break your code. Be sure to check the this page : 
http://code.google.com/p/deap/wiki/Break to find out the minor modifications that
are needed to get your code fully functionnal with 0.8.

We are also proud to announce the creation of the DEAP speed project which aims
at benchmarking on a daily basis the execution time of every examples provided
with DEAP. Details of the project and the results are available at the following
web page.

http://deap.gel.ulaval.ca/speed

Your feedback and comments are welcome at http://goo.gl/2HiO1 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