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A Recurrent Multivalued Neural Network for the N-Queens Problem

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Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence (IWANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2084))

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Abstract

This paper presents a multivalued Hopfield-type neural network as a method for solving combinatorial optimization problems with a formulation free of fine-tuning parameters.

As benchmark of the performance of the network we have used N-Queen problems. Computer simulations confirm that this network obtains good results when is compared with other neural networks.

It is shown also that different dynamics are easily formulated for the network leading to obtain more sophisticated algorithms with better performance.

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Author information

Authors and Affiliations

  1. Departamento de Matemática Aplicada, Universidad de Málaga Complejo Tecnológico, Campus Universitario de Teatinos s/n, 29079, Málaga

    Enrique Mérida

  2. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga Complejo Tecnológico, Campus Universitario de Teatinos s/n, 29079, Málaga

    José Muñoz & Rafaela Benétez

Authors
  1. Enrique Mérida
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  2. José Muñoz
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  3. Rafaela Benétez
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, s/n., Madrid, 28040, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Mérida, E., Muñoz, J., Benétez, R. (2001). A Recurrent Multivalued Neural Network for the N-Queens Problem. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_62

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  • DOI: https://doi.org/10.1007/3-540-45720-8_62

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42235-8

  • Online ISBN: 978-3-540-45720-6

  • eBook Packages: Springer Book Archive

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