Publications

DEAP: Evolutionary Algorithms Made Easy

Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau and Christian Gagné

Abstract - DEAP 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 frameworks. Freely available with extensive documentation at http://deap.gel.ulaval.ca, DEAP is an open source project under an LGPL license.

Bibtex:

@article{Fortin944,
author    = { Félix-Antoine Fortin and François-Michel De Rainville and Marc-André Gardner and Marc Parizeau and Christian Gagné },
title     = { DEAP: Evolutionary Algorithms Made Easy },
volume    = { 2171--2175 },
number    = { 13 },
year      = { 2012 },
month     = { jul },
journal   = { Journal of Machine Learning Research }
}

Dernière modification: 2012/06/19 par fmder1

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