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Evolutionary Computing. Springer.
20:
Querying the CPPN to determine the connection weight between two neurons as a function of their position in space. Note sometimes the distance between them is also passed as an argument.
427:
J. Gauci and K. O. Stanley, “A case study on the critical role of geometric regularity in machine learning,” in AAAI (D. Fox and C. P. Gomes, eds.), pp. 628–633, AAAI Press, 2008.
43:. 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 (
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Clune, Jeff; Beckmann, Benjamin E.; McKinley, Philip K.; Ofria, Charles (2010-01-01). "Investigating whether hyperNEAT produces modular neural networks".
437:
Risi, Sebastian; Stanley, Kenneth O. (2013-01-01). "Confronting the challenge of learning a flexible neural controller for a diversity of morphologies".
488:
Clune, J.; Beckmann, B. E.; Ofria, C.; Pennock, R. T. (2009-05-01). "Evolving coordinated quadruped gaits with the HyperNEAT generative encoding".
539:
Clune, Jeff; Ofria, Charles; Pennock, Robert T. (2009-01-01). "The sensitivity of HyperNEAT to different geometric representations of a problem".
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Performance of Indirect Encoding Across the Continuum of Regularity".
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Suchorzewski, Marcin; Clune, Jeff (2011-01-01). "A novel generative encoding for evolving modular, regular and scalable networks".
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69:. HyperNEAT has recently been extended to also evolve plastic ANNs and to evolve the location of every neuron in the network.
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Clune, Jeff; Lipson, Hod (2011-11-01). "Evolving 3D Objects with a
Generative Encoding Inspired by Developmental Biology".
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Verbancsics, Phillip; Stanley, Kenneth O. (2011-01-01). "Constraining connectivity to encourage modularity in HyperNEAT".
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Genetic and evolutionary computation
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Stanley, Kenneth O.; D'Ambrosio, David B.; Gauci, Jason (2009-01-14).
119:"A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks"
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888:. GECCO '11. New York, NY, USA: ACM. pp. 1483–1490.
837:. GECCO '11. New York, NY, USA: ACM. pp. 1523–1530.
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786:. GECCO '10. New York, NY, USA: ACM. pp. 635–642.
543:. GECCO '09. New York, NY, USA: ACM. pp. 675–682.
441:. GECCO '13. New York, NY, USA: ACM. pp. 255–262.
391:. GECCO '08. New York, NY, USA: ACM. pp. 819–826.
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Investigating the Evolution of Modular Neural Networks
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Advances in Artificial Life. Darwin Meets von Neumann
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Evolving the Neural Geometry and Plasticity of an ANN
330:Risi, Sebastian; Stanley, Kenneth O. (2012-08-31).
1043:"Evolutionary Complexity Research Group at UCF"
490:2009 IEEE Congress on Evolutionary Computation
35:(ANNs) with the principles of the widely used
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47:), which are used to generate the images for
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37:NeuroEvolution of Augmented Topologies
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39:(NEAT) algorithm developed by
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84:Controlling Legged Robots
81:Checkers board evaluation
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174:Evolutionary Computation
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492:. pp. 2764–2771.
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1048:NEAT Project Homepage
1038:Ken Stanley's website
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1033:HyperNEAT Users Page
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78:Multi-agent learning
73:Applications to date
25:Hypercube-based NEAT
1168:Genetic algorithms
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