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Designing Multiple ANNs with Evolutionary Development: Activity Dependence

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Book cover Genetic Programming Theory and Practice XVIII

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

We use Cartesian genetic programming to evolve developmental programs that construct neural networks. One program represents the neuron soma and the other the dendrite. We show that the evolved programs can build a network from which multiple conventional ANNs can be extracted each of which can solve a different computational problem. We particularly investigate the utility of activity dependence (AD), where the signals passing through dendrites and neurons affect their properties.

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Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets.html.

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Author information

Authors and Affiliations

  1. University of York, Heslington, York, YO10 5DD, UK

    Julian Francis Miller

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  1. Julian Francis Miller
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Tecnológico Nacional de México/IT de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan Winkler

  4. Evolution Enterprise, Ann Arbor, MI, USA

    Bill Worzel

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Miller, J.F. (2022). Designing Multiple ANNs with Evolutionary Development: Activity Dependence. In: Banzhaf, W., Trujillo, L., Winkler, S., Worzel, B. (eds) Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-16-8113-4_9

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  • DOI: https://doi.org/10.1007/978-981-16-8113-4_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8112-7

  • Online ISBN: 978-981-16-8113-4

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