This project is no longer under active development. The forks by @drallensmith and @bennr01 have been extended beyond this implementation a great deal, so those might be better starting points if you need more features than what you see here.

NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural networks. This project is a pure-Python implementation of NEAT with no dependencies beyond the standard library. It was forked from the excellent project by @MattKallada, and is in the process of being updated to provide more features and a (hopefully) simpler and documented API.

For further information regarding general concepts and theory, please see Selected Publications on Stanley's website.

neat-python is licensed under the 3-clause BSD license.

If you want to try neat-python, please check out the repository, start playing with the examples (examples/xor is a good place to start) and then try creating your own experiment.

The documentation is available on Read The Docs.

Here are APA and Bibtex entries you can use to cite this project in a publication. The listed authors are the maintainers of all iterations of the project up to this point.

APA

McIntyre, A., Kallada, M., Miguel, C. G., & Feher de Silva, C. neat-python [Computer software]

Bibtex

@software{McIntyre_neat-python,
author = {McIntyre, Alan and Kallada, Matt and Miguel, Cesar G. and Feher de Silva, Carolina},
title = {{neat-python}}
}