FORK STATUS

This is a fork from CodeReclaimers. The main focus is Non-dominated Sorting for Multiobjective Fitness. That means having more than one fitness value that should be optimized. This is done throught the implementation of NSGA-II as a Reproduction method. More details on the neat/nsga2/ readme.

The current repository also presents a hoverboard game/simulation to be used as a problem for testing the NSGA-II feature, as well as examples for training it with and without NSGA-II. Check the readme on examples/nsga2 for more details.

I've tried keeping the minimal amount of change to the core library, so merging to the main fork should be easy. All these changes are backwards-compatible.

About

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.

Getting Started

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.

Citing

Here is a Bibtex entry 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.

@misc{neat-python,
    Title = {neat-python},
    Author = {Alan McIntyre and Matt Kallada and Cesar G. Miguel and Carolina Feher da Silva},
    howpublished = {\url{https://github.com/CodeReclaimers/neat-python}}   
  }