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Multilevel Genetic Algorithm for the Complete Development of ANN

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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

The utilization of Genetic Algorithms (GA) in the development of Artificial Neural Networks is a very active area of investigation. The works that are being carried out at present not only focus on the adjustment of the weights of the connections, but also they tend, more and more, to the development of systems which realize tasks of design and training, in parallel. To cover these necessities and, as an open platform for new developments, in this article it is shown a multilevel GA architecture which establishes a difference between the design and the training tasks. In this system, the design tasks are performed in a parallel way, by using different machines. Each design process has associated a training process as an evaluation function. Every design GA interchanges solutions in such a way that they help one each other towards the best solution working in a cooperative way during the simulation.

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Author information

Authors and Affiliations

  1. Univ. da Coruña, Facultad Informática, Campus Elviña, 15071, A Coruña, Spain

    Julian Dorado, Antonino Santos & Juan R. Rabuñal

Authors
  1. Julian Dorado
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  2. Antonino Santos
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  3. Juan R. Rabuñal
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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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Dorado, J., Santos, A., Rabuñal, J.R. (2001). Multilevel Genetic Algorithm for the Complete Development of ANN. 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_86

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  • DOI: https://doi.org/10.1007/3-540-45720-8_86

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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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