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HyperNEAT: The First Five Years

  • David B. D’Ambrosio
  • Jason Gauci
  • Kenneth O. Stanley
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 557)

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.

Keywords

Hide Node Connectivity Pattern Quadruped Robot Direct Encode Food Gathering 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Much of the work on HyperNEAT at the Evolutionary Complexity Research Group at the University of Central Florida was supported by DARPA through its Computer Science Study Group program, including Phases 1, 2, and 3 (grants HR0011- 08-1-0020, HR0011-09-1-0045 and N11AP20003). This chapter does not necessarily reflect the position or policy of the government, and no official endorsement should be inferred.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • David B. D’Ambrosio
    • 1
  • Jason Gauci
    • 1
  • Kenneth O. Stanley
    • 1
  1. 1.University of Central FloridaOrlandoUSA

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