Hands-On Neuroevolution with Python

NEAT algorithm overview

The method of NEAT for evolving complex ANNs was designed to reduce the dimensionality of the parameter search space through the gradual elaboration of the ANN's structure during evolution. The evolutionary process starts with a population of small, simple genomes (seeds) and gradually increases their complexity over generations.

The seed genomes have a very simple topology: only input, output, and bias neurons are expressed. No hidden nodes are introduced into the seed from the beginning to guarantee that the search for a solution starts in the lowest-dimensional parameter space (connection weights) possible. With each new generation, additional genes are introduced, expanding the solution search space by presenting ...