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

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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).




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[1] Nicolas Schweighofer and Kenji Doya. Meta-learning in reinforcement learning. Neural Networks, 16(1):5–9, 2003. [2] Sepp Hochreiter, A Steven Younger, and Peter R Conwell. Learning to learn using gradient descent. In International Conference on Artificial Neural Networks, pages 87–94. Springer, 2001.

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