Hypercube-based NEAT, or HyperNEAT, is a generative encoding that evolves

artificial neural networks In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure and functions of biological neural networks. A neural network consists of connected ...

(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 fo
Picbreeder.org
and shapes fo
EndlessForms.com
. 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 Robo
video
* 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

References

External links

* * * * * * * Evolutionary algorithms and artificial neuronal networks Evolutionary computation Genetic algorithms {{bioinformatics-stub