DEAP 0.8: Distributed Evolutionary Algorithms in Python

a library for doing Distributed Evolutionary Algorithms in Python. You can download it here.

---

Date:

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

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 :

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.

deap.gel.ulaval.ca/speed

Your feedback and comments are welcome at goo.gl/2HiO1 or deap-users at googlegroups dot com. You can also follow us on Twitter @deapdev, and on our blog deapdev.wordpress.com/.

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