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Genetic Synthesis of Task-Oriented Neural Networks

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Book cover Artificial Neural Nets and Genetic Algorithms

Abstract

A stochastic search technique based on genetic algorithms for design of task-oriented neural networks is described in the paper. Although the theory of the algorithms is clear, its implementation in the design of neural structures is not yet well investigated. With the help of two case studies, we want to outline the new design approach.

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References

  1. Montana, D.J., Davis, L.: Training feedforward neural networks using genetic algorithms. Proc. of the 11-th Int. Joint Conf. on AI, pp 762–766, 1989

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  6. Tadel, M., Grabensek L., Dobnikar A.: Neural Networks without design — evolution with genetic algorithms and genotypes of variable length, int. report (in Slovenian language), LASPP-FER, Ljubljana, 1993.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Ljubljana, Trzaska 25, 61000, Ljubljana, Slovenia

    Andrej Dobnikar

Authors
  1. Andrej Dobnikar
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© 1995 Springer-Verlag/Wien

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Dobnikar, A. (1995). Genetic Synthesis of Task-Oriented Neural Networks. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_86

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  • DOI: https://doi.org/10.1007/978-3-7091-7535-4_86

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82692-8

  • Online ISBN: 978-3-7091-7535-4

  • eBook Packages: Springer Book Archive

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