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International Work-Conference on Artificial Neural Networks

IWANN 1999: Foundations and Tools for Neural Modeling pp 357–366Cite as

What does a peak in the landscape of a Hopfield associative memory look like?

  • Neural Modeling (Biophysical and Structural Models)
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1606))

Abstract

Performance of an evolutionary algorithm usually depends on the shape of fitness surface (landscape) defined on the solution space. Hence, learning a geometry of the surface is a very important issue to predict the performance of an evolutionary algorithm. In real world problem, however, it is usually very difficult to know the information of the surface such as the number and distribution of peaks on the surface, the height and width of the peaks and so on. In this paper, we address how we can learn the shape of a peak on the surface by using the fairly well studied Hopfield model of associative memory as a test function.

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Authors and Affiliations

  1. Nara Institute of Science and Technology, 1-11-13-604 Abeno, Hannan, 545-0021, Osaka, Japan

    Akira Imada

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  1. Akira Imada
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José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Imada, A. (1999). What does a peak in the landscape of a Hopfield associative memory look like?. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098192

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  • DOI: https://doi.org/10.1007/BFb0098192

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66069-9

  • Online ISBN: 978-3-540-48771-5

  • eBook Packages: Springer Book Archive

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