A Python package for implementing Compositional Pattern Producing Network (CPPN) variants for indirectly encoding artificial neural networks for neuroevolution and artificial life experiments.
- Purpose:
- Greater understanding of the HyperNEAT algorithm and indirect encoding variants for evolving a populations of neural networks.
- Practice in package management.
- General purpose research pipeline for appyling EPANN neuroevolution techniques to a variety of environments.
- Dissertation code and documentations.
You can find a collection of tutorials meant to introduce the major algorithms and features of the package in the docs/ subdirectory:
Start with the Tutorial Introduction