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Evolving Neural Networks: Selected Medical Applications and the Effects of Variation Operators

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Modeling and Simulation: Theory and Practice

Abstract

Evolutionary algorithms can be used to train and design neural networks for medical applications. This paper reviews some recent efforts in breast cancer detection using evolutionary neural networks. The results obtained are discussed in relation to other methods for analyzing similar data. Additional basic research data are presented that investigate the use of alternative forms of variation on neural networks (e.g., mutation and recombination). Mention is given to the inspiration that Walter Karplus provided to the author in applying computational intelligence methods to practical problems in medicine and other disciplines.

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Author information

Authors and Affiliations

  1. Natural Selection, Inc., 3333 N. Torrey Pines Ct., Suite 200, La Jolla, CA, 92037, USA

    David B. Fogel

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  1. David B. Fogel
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Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, California, USA

    George A. Bekey

  2. University of California, Los Angeles, California, USA

    Boris Y. Kogan

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Fogel, D.B. (2003). Evolving Neural Networks: Selected Medical Applications and the Effects of Variation Operators. In: Bekey, G.A., Kogan, B.Y. (eds) Modeling and Simulation: Theory and Practice. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0235-7_17

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  • DOI: https://doi.org/10.1007/978-1-4615-0235-7_17

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4979-2

  • Online ISBN: 978-1-4615-0235-7

  • eBook Packages: Springer Book Archive

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