github.com/whitewolf-studio/neat (go): Package neat provides an implementation of NeuroEvolution of Augmenting Topologies (NEAT) as a plug-and-play framework, which can be used by simply adding and appropriate configuration and an evaluation function.  NEAT (NeuroEvolution of Augmenting Topologies) is a neuroevolution algorithm by Dr. Kenneth O. Stanley which evolves not only neural networks' weights but also their topologies. This method starts the evolution process with genomes with minimal structure, then complexifies the structure of each genome as it progresses. You can read the original paper from here: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf  This NEAT package is as simple as plug and play. All you have to do is to create a new instance of NEAT, given the configuration from a JSON file, for which the template is provided below, and an evaluation method of a neural network, and run.  Now that you have the configuration JSON file is ready as `config.json`, we can start experiment with NEAT. Below is an example XOR experiment.
Keywords: genetic-algorithm|go|neat|neural-network|neuroevolution|recurrent-neural-networks|reinforcement-learning|topologies