NEAT is an algorithm that builds neural networks following an incremental and evolutionary process. It uses a genetic algorithm to evolve networks. In the very early generations, neural networks are very simple, composed of a few nodes and connections. However, complexity is added in each generation. NEAT supports a number of mutations, and these mutations may add new nodes or new connections. As such, networks can only become more complex over time.
NEAT was proposed in 2002 by Kenneth O. Stanley and Risto Miikkulainen in their article titled, “Evolving Neural Networks ...
