There are 1 repository under augmenting-topologies topic.
Genetic learning algorithm implementation for simulations, games, or general machine learning problems
NEAT (NeuroEvolution of Augmentic Topologies) C++ Library Algorithm Implementation
Using neural evolution of augmenting topologies developed a program based on computer vision for recognizing traffic lights in real time environment.
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
Implementation of NEAT algorithm, based on "Evolving Neural Networks through Augmenting Topologies" by Kenneth O. Stanley and Risto Miikkulainen
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.
Neuroevolution through Augmenting Topologies
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.
Neuroscience-inspired optimization algorithm known as NeuroEvolution of Augmenting Topologies (NEAT)