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Cooperative Co-evolution of Multilayer Perceptrons

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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

Co-evolution is a posible solution to the problem of simultaneous optimization of artificial neural network and training agorithm parameters, due to its ability to deal with vast search spaces. Moreover, this scheme is recommendable when the optimization problem is decomposable in subcomponents.

In this paper an approach to cooperative co-evolutionary optimisation of multilayer perceptrons, that improves the G-Prop genetic back-propagation algorithm, is presented.

Obtained results show that this co-evolutionary version of G-Prop obtains similar or better results needing much fewer training epochs and thus using much less time than the sequential versions.

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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

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

Authors
  1. P.A. Castillo
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  2. M.G. Arenas
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  3. J.J. Merelo
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  4. G. Romero
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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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Castillo, P., Arenas, M., Merelo, J., Romero, G. (2003). Cooperative Co-evolution of Multilayer Perceptrons. 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_46

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  • DOI: https://doi.org/10.1007/3-540-44868-3_46

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40210-7

  • Online ISBN: 978-3-540-44868-6

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