Hypercube-based NEAT, or HyperNEAT, 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. 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 (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 and to evolve the location of every neuron in the network.
Applications to date
- Multi-agent learning
- Checkers board evaluation
- Controlling Legged Robotsvideo
- Comparing Generative vs. Direct Encodings
- Investigating the Evolution of Modular Neural Networks
- Evolving Objects that can be 3D-printed
- Evolving the Neural Geometry and Plasticity of an ANN