DEAP, Distributed Evolutionary Algorithms in Python, Framework for Rapid Prototyping
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas, seeking to make algorithms explicit and data structures transparent. Free Download.
By Félix-Antoine Fortin, Feb 20, 2014.
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/.
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