The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to evolve and train Artificial Neural Networks without error back propagation

Using neural evolution of augmenting topologies developed a program based on computer vision for recognizing traffic lights in real time environment.

A java implementation of NEAT(NeuroEvolution of Augmenting Topologies ) from scratch for the generation of evolving artificial neural networks. Only for educational purposes.

Genetic learning algorithm implementation for simulations, games, or general machine learning problems

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

A compact implementation of NEAT (NeuroEvolution of Augmentic Topologies) algorithm on C++ for small programs/projects.

NEAT (NeuroEvolution of Augmentic Topologies) C++ Library Algorithm Implementation

Neuroevolution through Augmenting Topologies

An implementation of the NEAT (Neuroevolution through augmenting topologies) algorithm in Java. Originally found at http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf

Neuroscience-inspired optimization algorithm known as NeuroEvolution of Augmenting Topologies (NEAT)

A humple implementation of the NeuroEvolution of Augmenting Topologies[NEAT] algorithm written purely in Python3.

An AI that learns how to play flappy bird, using NEAT (NeuroEvolution of Augmenting Topologies), essentially taking the best attributes from different Genomes of Birds to end up with birds that are better at the game.

"Neuro Evolution of Augmenting Topologies"

This is a neuro-evolution of augmenting topologies library. It uses a genetic algorithm to evolve neural networks. This is useful when you don't have a dataset to train your neural network, for example when you need an agent to interact with an environment or to learn to play some games.

C++ ES-HyperNEAT algorithm implementation

Implementation of NEAT algorithm, based on "Evolving Neural Networks through Augmenting Topologies" by Kenneth O. Stanley and Risto Miikkulainen

Automatic Milking Systems Problem: Utilizing Neuroevolutionary Algorithms to infer milk components