Welcome to NEAT-Python’s documentation!¶
NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural networks. NEAT-Python is a pure Python implementation of NEAT, with no dependencies other than the Python standard library.
Note: Some of the sample code has other dependencies; please see each sample’s README file for additional details and installation/setup instructions.
Support for HyperNEAT and other extensions to NEAT is planned once the fundamental NEAT implementation is more complete and stable.
If you encounter any confusing or incorrect information in this documentation, please open an issue in the GitHub project.
- NEAT Overview
- Configuration file description
- Overview of the basic XOR example (xor2.py)
- Customizing Behavior
- Overview of builtin activation functions
- Continuous-time recurrent neural network implementation
- Genome Interface