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DEAP release 0.9.0

Dear ECers,

We are proud to announce the release of DEAP 0.9.0.
You can download a copy of this release at the following web page.

http://deap.googlecode.com

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.

This release includes :
– major overhaul of the genetic programming with significant speed increase;
– new state of the art operators to control bloat in GP;
– several new examples from diverse fields;
– organization of the examples by category;
– examples are now compatible with Python 2 and 3 out of the box;
– and several other changes.

A major change for DEAP is that from 0.9.0, the easy distribution module DTM
will be replaced by SCOOP (Scalable COncurrent Operations in Python), a
distributed task module allowing concurrent parallel programming on various
environments, from heterogeneous grids to supercomputers. This new project is
led by Yannick Hold-Geoffroy (@yannickhold) in close association with DEAP’s
developers. You can download the last release at the following web page.

http://scoop.googlecode.com/

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 https://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

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DEAP 0.8 released

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 https://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

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