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Genetic Algorithm Visualization Using Self-organizing Maps

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Parallel Problem Solving from Nature — PPSN VII (PPSN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2439))

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Abstract

This paper gives an overview of evolutionary computation visualization and describes the application of visualization to some well known multidimensional problems. Self-Organizing Maps (SOM) are used for multidimensional scaling and projection. We show how different ways of training the SOM make it more or less adequate for the visualization task.

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Author information

Authors and Affiliations

  1. Department of Architecture and Computer Technology, University of Granada, Campus de Fuentenueva, E. 18071, Granada, Spain

    G. Romero, J. J. Merelo, P. A. Castillo, J. G. Castellano & M. G. Arenas

Authors
  1. G. Romero
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  2. J. J. Merelo
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  3. P. A. Castillo
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  4. J. G. Castellano
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  5. M. G. Arenas
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Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Informática, 18071, Granada, Spain

    Juan Julián Merelo Guervós

  2. Department of Informatics, Technological Educational Institute of Thessaloniki, 54101, Thessaloniki, Greece

    Panagiotis Adamidis

  3. Department of Informatics XI, University of Dortmund, 44221, Dortmund, Germany

    Hans-Georg Beyer  & Hans-Paul Schwefel  & 

  4. Department of Signal Theory and Communications, Universidad Carlos III, Madrid, Spain

    José-Luis Fernández-Villacañas

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© 2002 Springer-Verlag Berlin Heidelberg

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Romero, G., Merelo, J.J., Castillo, P.A., Castellano, J.G., Arenas, M.G. (2002). Genetic Algorithm Visualization Using Self-organizing Maps. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_43

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  • DOI: https://doi.org/10.1007/3-540-45712-7_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44139-7

  • Online ISBN: 978-3-540-45712-1

  • eBook Packages: Springer Book Archive

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