Everything about 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. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: HyperNEATA 5-minute orientation
- Pick up a classic book: Applications of Evolutionary ComputationThe most-reprinted book on the subject
- Read the landmark paper: Locomotion Control of 4 Legged Robot Using HyperNEATCited 2 times
- Go sideways: NeuroevolutionA closely related topic
- Go sideways: Effective fitnessA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
đź“š Books worth reading
Applications of Evolutionary ComputationAnna Isabel Esparcia-Alcázar · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
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