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 Python implementation of NEAT.

The actual NEAT implementation is currently pure Python with no dependencies other than the Python standard library. The visualize module requires graphviz, NumPy, and matplotlib, but it is not necessary to install these packages unless you want to make use of these visualization utilities.

If you need an easy performance boost, JIT-enabled PyPy does a fantastic job, and may give you a ~10x speedup over CPython.

Please note: the package and its usage may change significantly while it is still in alpha status. Updating to the most recent version is almost certainly going to break your code until the version number approaches 1.0.

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:

• NEAT Overview
• Configuration file format
  • [phenotype] section
  • [genetic] section
  • [genotype compatibility] section
  • [species] section
• Overview of the basic XOR example (xor2.py)
  • Fitness function
  • Running NEAT
  • Getting the results
  • Visualizations

Indices and tables

• Index
• Module Index
• Search Page