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Co-Evolutionary Algorithm for RBF by Self- Organizing Population of neurons

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Computational Methods in Neural Modeling (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2686))

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Abstract

This paper presents a new evolutionary procedure to design optimal networks of Radial Basis Functions (RBFs). It defines a self-organizing process into a population of RBFs based on the estimation of the fitness for each neuron in the population, and on the use of operators that, according to a set of fuzzy rules, transform the RBFs. This way, it has been possible to define cooperation, speciation, and niching features in the evolution of the population.

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Author information

Authors and Affiliations

  1. Departamento de Informática, Universidad de Jaén, Granada

    A. J. Rivera & M. J. del Jesús

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Granada

    J. Ortega & I. Rojas

Authors
  1. A. J. Rivera
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  2. J. Ortega
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  3. I. Rojas
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  4. M. J. del Jesús
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Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Rivera, A.J., Ortega, J., Rojas, I., del Jesús, M.J. (2003). Co-Evolutionary Algorithm for RBF by Self- Organizing Population of neurons. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_60

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  • DOI: https://doi.org/10.1007/3-540-44868-3_60

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40210-7

  • Online ISBN: 978-3-540-44868-6

  • eBook Packages: Springer Book Archive

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