CPPNs
Compositional Pattern Producing Networks (CPPNs) are a form of artificial neural network used as a generative encoding in neuroevolution. Instead of directly encoding a fixed network, a CPPN acts as a compact program that, when queried with input coordinates, outputs parameters for another network or for a pattern.
CPPNs are typically feedforward networks whose nodes apply a variety of activation functions, such as sigmoid,
A central application is the indirect encoding of large patterned networks through geometry. In HyperNEAT, for
Uses include evolving neural networks for robotics and control tasks, as well as generating procedural content
Advantages include compact, scalable encodings and the ability to exploit geometric regularities; limitations involve bias toward