Building a population of models that trade crypto and mutate iteratively

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

NEAT (NeuroEvolution of Augmenting Topologies) implemented in Go

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

The implementation of evolvable-substrate HyperNEAT algorithm in GO language. ES-HyperNEAT is an extension of the original HyperNEAT method for evolving large-scale artificial neural networks.

Just another NEAT implementation.

This library use a genetic algorithm to fit a neural network weights. 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.

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.