The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to evolve and train Artificial Neural Networks without error back propagation
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Updated
Jun 24, 2024 - Go
The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to evolve and train Artificial Neural Networks without error back propagation
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
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.
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.
Building a population of models that trade crypto and mutate iteratively
Just another NEAT implementation.
NEAT (NeuroEvolution of Augmenting Topologies) implemented in Go
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