Welcome to NEvoPy’s documentation!¶
NEvoPy is an open source neuroevolution framework for Python. It provides a simple and intuitive API for researchers and enthusiasts in general to quickly tackle machine learning problems using neuroevolutionary algorithms. NEvoPy is optimized for distributed computing and has compatibility with TensorFlow.
Currently, the neuroevolutionary algorithms implemented by NEvoPy are:
NEAT (NeuroEvolution of Augmenting Topologies), a powerful method by Kenneth O. Stanley for evolving neural networks through complexification;
the standard fixed-topology approach to neuroevolution, with support to TensorFlow and deep neural networks.
Note, though, that there’s much more to come!
In addition to providing high-performance implementations of powerful neuroevolutionary algorithms, such as NEAT, NEvoPy also provides tools to help you more easily implement your own algorithms.
Neuroevolution, a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANNs), is one of the most interesting and unexplored fields of machine learning. It is a vast and expanding area of research that holds many promises for the future.
If you encounter any confusing, incomplete or incorrect information in this project, please open an issue in our GitHub project.
- NEvoPy Overview
- Modules and Subpackages