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2014 | OriginalPaper | Chapter

5. HyperNEAT: The First Five Years

Authors : David B. D’Ambrosio, Jason Gauci, Kenneth O. Stanley

Published in: Growing Adaptive Machines

Publisher: Springer Berlin Heidelberg

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Abstract

HyperNEAT, which stands for Hypercube-based NeuroEvolution of Augmenting Topologies, is a method for evolving indirectly-encoded artificial neural networks (ANNs) that was first introduced in 2007. By exploiting a unique indirect encoding called Compositional Pattern Producing Networks (CPPNs) that does not require a typical developmental stage, HyperNEAT introduced several novel capabilities to the field of neuroevolution (i.e. evolving artificial neural networks). Among these, (1) large ANNs can be compactly encoded by small genomes, (2) the size and resolution of evolved ANNs can scale up or down even after training is completed, and (3) neural structure can be evolved to exploit problem geometry. Five years after its introduction, researchers have leveraged these capabilities to produce a broad range of successful experiments and extensions that highlight the potential for future research to build further on the ideas introduced by HyperNEAT. This chapter reviews these first 5 years of research that builds upon this approach, and culminates with thoughts on promising future directions.

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Title: HyperNEAT: The First Five Years
Authors: David B. D’Ambrosio
Jason Gauci
Kenneth O. Stanley
Publisher: Springer Berlin Heidelberg
Book: Growing Adaptive Machines
Print ISBN: 978-3-642-55336-3

Electronic ISBN: 978-3-642-55337-0

Copyright Year: 2014
DOI: https://doi.org/10.1007/978-3-642-55337-0_5

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