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An evolutionary algorithm for designing feedforward neural networks

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Evolutionary Programming VII (EP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

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Abstract

This paper presents a new approach to evolutionary artificial neural networks, based on the integration of feedforward neural networks, messy genetic algorithms (GAs), and singular value decomposition (SVD). The set of competing hidden nodes with variable number of connections from the input layer represents an evolving neural network. Selection of hidden nodes is based on their estimation via SVD. The resulting singular values are used to determine significance of hidden nodes for the network's output. To represent connectivity of hidden nodes and to process the topology of connections between input and hidden layers, we employ the approach of messy GAs. This establishes a framework for processing strings of variable length which codes this topology and allows one to search for useful combinations of input variables. The proposed approach is tested using sonar data classification.

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Authors and Affiliations

  1. Los Alamos National Laboratory, MS E541, 87545, Los Alamos, NM

    Alexei N. Skourikhine

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  1. Alexei N. Skourikhine
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V. W. Porto N. Saravanan D. Waagen A. E. Eiben

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© 1998 Springer-Verlag Berlin Heidelberg

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Skourikhine, A.N. (1998). An evolutionary algorithm for designing feedforward neural networks. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040814

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  • DOI: https://doi.org/10.1007/BFb0040814

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

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