Welcome to CT-NEAT-Python’s documentation!

NEAT is a method developed by Kenneth O. Stanley for evolving arbitrary neural networks. CT-NEAT is an extension of NEAT for evolving continuous-time recurrent neural networks (CTRNNs), also developed by Stanley. CT-NEAT-Python is a Python implementation of CT-NEAT, with minimal dependencies beyond the standard library.

Currently this library supports Python versions 3.8 through 3.13, as well as PyPy 3.

Many thanks to the authors of the original implementation: Cesar Gomes Miguel, Carolina Feher da Silva, and Marcio Lobo Netto. And the later work and development by CodeReclaimers (on GitHub) - Alan and Kallada McIntyre, Matt and Miguel.

Note

Some of the example code has additional dependencies. For your convenience there is a conda environment YAML file in the examples directory you can use to set up an environment that will support all of the current examples. TODO: Improve README.md file information for the examples.

For further information regarding general concepts and theory, please see Selected Publications on Stanley’s website, or his recent AMA on Reddit.

If you encounter any confusing or incorrect information in this documentation, please open an issue in the GitHub project.

Contents:

Indices and tables