The examples are currently being moved into a separate repository (and Python package) from the NEAT library itself. During this time if you use the code from the GitHub repository you may find that examples are missing and/or not working.

NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural networks. This project is a Python implementation of NEAT. 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.

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, which is still a work in progress, is available on Read The Docs.