DEAP: Evolutionary Algorithms Made Easy
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.
Cite
Text
Fortin et al. "DEAP: Evolutionary Algorithms Made Easy." Machine Learning Open Source Software, 2012.Markdown
[Fortin et al. "DEAP: Evolutionary Algorithms Made Easy." Machine Learning Open Source Software, 2012.](https://mlanthology.org/mloss/2012/fortin2012jmlr-deap/)BibTeX
@article{fortin2012jmlr-deap,
title = {{DEAP: Evolutionary Algorithms Made Easy}},
author = {Fortin, Félix-Antoine and De Rainville, François-Michel and Gardner, Marc-André and Parizeau, Marc and Gagné, Christian},
journal = {Machine Learning Open Source Software},
year = {2012},
pages = {2171-2175},
volume = {13},
url = {https://mlanthology.org/mloss/2012/fortin2012jmlr-deap/}
}