Genetic automated machine learning assistant


== Abstract ==

GAMA is an AutoML package for end-users and AutoML researchers. It uses genetic programming to efficiently generate optimized machine learning pipelines given specific input data and resource constraints. A machine learning pipeline contains data preprocessing as well as a machine learning algorithm, with fine-tuned hyperparameter settings.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_920112968-beopen1477-1612-document.pdf</pdf>


== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.21105/joss.01132 http://dx.doi.org/10.21105/joss.01132] under the license http://creativecommons.org/licenses/by/4.0/

* [https://doi.org/10.21105/joss.01132 https://doi.org/10.21105/joss.01132] under the license http://creativecommons.org/licenses/by/4.0

* [https://research.tue.nl/nl/publications/gama(d1d8f995-689d-47fb-aab5-cead00d0250b).html https://research.tue.nl/nl/publications/gama(d1d8f995-689d-47fb-aab5-cead00d0250b).html] under the license cc-by

* [http://www.theoj.org/joss-papers/joss.01132/10.21105.joss.01132.pdf http://www.theoj.org/joss-papers/joss.01132/10.21105.joss.01132.pdf],
: [http://dx.doi.org/10.21105/joss.01132 http://dx.doi.org/10.21105/joss.01132]

* [https://dblp.uni-trier.de/db/journals/corr/corr2007.html#abs-2007-04911 https://dblp.uni-trier.de/db/journals/corr/corr2007.html#abs-2007-04911],
: [https://joss.theoj.org/papers/10.21105/joss.01132 https://joss.theoj.org/papers/10.21105/joss.01132],
: [https://www.theoj.org/joss-papers/joss.01132/10.21105.joss.01132.pdf https://www.theoj.org/joss-papers/joss.01132/10.21105.joss.01132.pdf],
: [http://joss.theoj.org/papers/74cfa7e5796270cd2a96005370973c71 http://joss.theoj.org/papers/74cfa7e5796270cd2a96005370973c71],
: [https://research.tue.nl/en/publications/gama-genetic-automated-machine-learning-assistant https://research.tue.nl/en/publications/gama-genetic-automated-machine-learning-assistant],
: [https://doi.org/10.21105/joss.01132 https://doi.org/10.21105/joss.01132],
: [https://www.narcis.nl/publication/RecordID/oai%3Apure.tue.nl%3Apublications%2Fd1d8f995-689d-47fb-aab5-cead00d0250b https://www.narcis.nl/publication/RecordID/oai%3Apure.tue.nl%3Apublications%2Fd1d8f995-689d-47fb-aab5-cead00d0250b],
: [https://academic.microsoft.com/#/detail/2912436245 https://academic.microsoft.com/#/detail/2912436245] under the license http://creativecommons.org/licenses/by/4.0/