HyperNEAT
Generative encoding that evolves artificial neural networks
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 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 been extended to also evolve plastic ANNs and to evolve the location of every neuron in the network.
01Applications to date
- Multi-agent learning
- Checkers board evaluation
- Controlling Legged Robots 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
Sources and credits
This article is adapted from the Wikipedia article “HyperNEAT”, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.
Images, from Wikimedia Commons:
- HyperNEAT query connection.png by EvilxFish, CC BY-SA 4.0
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