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DEAP release 0.7

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.


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 https://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

Categories: Uncategorized
  1. mathdr
    September 27, 2011 at 12:55 AM

    Hi.I just recently (three days ago) found your DEAP site on Google code. I just wanted to thank you! (I wrote a complete prefix gene expression program in python over a year ago, but your implementation is WAY faster than mine!).

    Quick question: How do you accept extensions? That is, if I wanted to add a subsample fitness capability, how could I share that with users?

    Please advise,


    • September 27, 2011 at 9:44 AM

      Hi Dan,

      I’m glad you like DEAP! Spread the Word ;).

      For your question, if you want to share your experience or some tricks with the users, we have a Google group : http://groups.google.com/group/deap-users. If you want to propose an extension for DEAP, post a message on the group, and share your code on either Bitbucket or Github (the gist functionality is pretty cool for those kind of things). We will review your proposal and come back to you quickly (it generally take less then a day). Finally, if you just want to interact quickly, you can do it via Twitter @deapdev.

      Thanks for your feedback,

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