DEAP 0.7 : Distributed Evolutionary Algorithms in Python

[Félix-Antoine Fortin]

Hi everyone,

We are proud to annouce the release of DEAP 0.7, 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

For those who wouldn't already know about the project, it is built
around two major parts, EAP and DTM.

EAP has been built using the Python and UNIX programming philosophies
in order to provide a transparent, simple and coherent environment for
implementing your favourite evolutionary algorithms. EAP is very easy
to use even for those who do not know much about the Python
programming language. EAP uses both the object oriented and functional
programming paradigm that are provided by Python in order to make
development simple and beautiful. It also contains more than 20
illustrative and diversified examples, to help newcomers to ramp up
very quickly in using this environment.

The D part of DEAP, called DTM, is under intense development and
currently available as an alpha version (0.2). DTM provides tools to
distribute workload evenly on a cluster or LAN of workstations, based
on MPI and TCP communication managers. The load balancing is based on
a new epidemiologic model. This unique model allows unique
possibilities, like tasks spawning other tasks that can be run on any
available workers.

This release includes a lot of new examples, a cleaner API, new
features like easy statistics computation and a benchmark module, new
variation methods for finer control on algorithms, and a few bug
fixes.

Your feedback and comments are welcome at <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