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DEAP: A Python Framework for Evolutionary Algorithms

François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau and Christian Gagné
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Abstract - 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. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronization and load balancing, only functional decomposition. Several examples illustrate the multiple properties of DEAP.

Bibtex:

@inproceedings{Rainville943,
author    = { François-Michel De Rainville and Félix-Antoine Fortin and Marc-André Gardner and Marc Parizeau and Christian Gagné },
title     = { DEAP: A Python Framework for Evolutionary Algorithms },
booktitle = { EvoSoft Workshop, Companion proc. of the Genetic and Evolutionary Computation Conference (GECCO 2012) },
year      = { 2012 },
month     = { July 07-11 },
web       = { http://dx.doi.org/10.1145/2330784.2330799 }
}

Last modification: 2012/06/12 by cgagne

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