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Evolving transfer functions for artificial neural networks

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Abstract

The paper describes a methodology for constructing transfer functions for the hidden layer of a back-propagation network, which is based on evolutionary programming. The method allows the construction of almost any mathematical form. It is tested using four benchmark classification problems from the well-known machine intelligence problems repository maintained by the University of California, Irvine. It was found that functions other than the commonly used sigmoidal function could perform well when used as hidden layer transfer functions. Three of the four problems showed improved test results when these evolved functions were used.

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Authors and Affiliations

  1. University of Colorado, Colorado Springs, CO, USA

    Marijke F. Augusteijn & Thomas P. Harrington

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  1. Marijke F. Augusteijn
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  2. Thomas P. Harrington
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Correspondence to Marijke F. Augusteijn.

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Augusteijn, M.F., Harrington, T.P. Evolving transfer functions for artificial neural networks. Neural Comput & Applic 13, 38–46 (2004). https://doi.org/10.1007/s00521-003-0393-9

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  • DOI: https://doi.org/10.1007/s00521-003-0393-9

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