Displaying 1 to 7 from 7 results

neat - NEAT (NeuroEvolution of Augmenting Topologies) implemented in Go

  •    Go

CURRENTLY NOT WORKING! There will be a further notice when it's updated.NEAT (NeuroEvolution of Augmenting Topologies) is a neuroevolution algorithm by Dr. Kenneth O. Stanley which evolves not only neural networks' weights but also their topologies. This method starts the evolution process with genomes with minimal structure, then complexifies the structure of each genome as it progresses. You can read the original paper from here.

rustneat - Rust Neat - NeuroEvolution of Augmenting Topologies

  •    Rust

This implementations uses a Continuous-Time Recurrent Neural Network (CTRNN) (Yamauchi and Beer, 1994).

goNEAT - The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to grow and teach Artificial Neural Networks without back propagation

  •    Go

This repository provides implementation of NeuroEvolution of Augmenting Topologies (NEAT) method written in Go language. The Neuroevolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuroevolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).




goNEAT_NS - This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

  •    Go

This repository provides implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization implemented in GoLang. The Neuro-Evolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuro-Evolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).

denser-models

  •    Python

Currently this codebase only works with python 2. The following libraries are needed: keras, numpy, and sklearn.

sharpneat - SharpNEAT - Evolution of Neural Networks. A C# .NET Framework.

  •    CSharp

If you would like to fund future work then donations are welcome via Open Collective or Patreon. NEAT is NeuroEvolution of Augmenting Topologies; an evolutionary algorithm devised by Kenneth O. Stanley.






We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.