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6391Distributed Evolutionary Algorithm in Python (DEAP) Turns 1.0

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  • f.derainville
    Feb 20 9:17 AM
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      Dear ECers,

      After more than 4 years of development, we are proud to announce the
      release of DEAP 1.0.0. You can download a copy of this release at the
      following web page.


      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.

      To get to know more about DEAP and the current release, we invite you
      to read the most recent article on DEAP published in SIGEvolution volume 6,
      issue 2, pp. 17-26.


      An IPython notebook version of the article is also available.


      This release includes:
      - Major overhaul of statistics computing and logging;
      - Ability to do Object Oriented Genetic Programming (OOGP);
      - Symbolic regression benchmarks for GP;
      - New tutorials and better documentation;
      - Several new examples from diverse fields;
      - and several other changes.

      Every changes of this release are detailed in the documentation.


      To help users translate code from 0.9.x to 1.0.0, we have also written
      a new porting guide that details every change required to use DEAP 1.0.


      Your feedback and comments are welcome at http://goo.gl/LZkdi4 or
      deap-users at googlegroups dot com. You can also follow us on Twitter @deapdev,
      and on our blog http://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
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