HyperNEAT

Hypercube-based NEAT, or HyperNEAT,[1] is a generative encoding that evolves artificial neural networks (ANNs) with the principles of the widely used NeuroEvolution of Augmented Topologies (NEAT) algorithm developed by Kenneth Stanley.

[2] It is a novel technique for evolving large-scale neural networks using the geometric regularities of the task domain.

It uses Compositional Pattern Producing Networks [3] (CPPNs), which are used to generate the images for Picbreeder.org Archived 2011-07-25 at the Wayback Machine and shapes for EndlessForms.com Archived 2018-11-14 at the Wayback Machine.

HyperNEAT has recently been extended to also evolve plastic ANNs [4] and to evolve the location of every neuron in the network.

This bioinformatics-related article is a stub.

Querying the CPPN to determine the connection weight between two neurons as a function of their position in space. Note sometimes the distance between them is also passed as an argument.