We already have the mating part with NeatAI. Link: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf http://nn.cs.utexas.edu/downloads/papers/stanley.ec

We already have the mating part with NeatAI. Link: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Essentially it's a neural network which is represented by a series of genes. Genetically, new genes come in by having new links form between neurons, disabled, or split (with an in between neuron). This, as you can see, allows for a wide range of network representations. Because these variations are tagged with id's sequentially, we can mate two neural networks by combining their genes in order. They never conflict, and will produce something similar to both parents.