• Source: HyperNEAT

    • 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


    References




    External links


    HyperNEAT Users Page
    Ken Stanley's website
    "Evolutionary Complexity Research Group at UCF"
    NEAT Project Homepage
    PicBreeder.org Archived 2021-04-17 at the Wayback Machine
    EndlessForms.com Archived 2018-11-14 at the Wayback Machine
    BEACON Blog: What is neuroevolution?

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