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Parallel genetic evolution of membership functions and rules for a fuzzy controller

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High-Performance Computing and Networking (HPCN-Europe 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1401))

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Abstract

Parallel Genetic Algorithms (PGAs), implemented on the APE100/Quadrics SIMD architecture, were applied to automatic design of membership functions and fuzzy rules for robotic control. They run multiple simultaneous searches, differently balancing exploration of the solution space and fine tune of the best solutions available at each generation. Migration spreads the best individuals of each population in local neighborhoods. The approach reduces the time required for fitness evaluation (each population has less individuals, decreases the generations required for acceptable solutions and increases the probability of identifying optimal solutions.

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References

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Authors and Affiliations

  1. Istituto Elaborazione Segnali ed Immagini - C.N.R., Via Amendola, 1665 - 70126, Bari, Italy

    G. Mondelli, G. Castellano, G. Attolico & C. Distante

Authors
  1. G. Mondelli
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  2. G. Castellano
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  3. G. Attolico
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  4. C. Distante
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Editor information

Peter Sloot Marian Bubak Bob Hertzberger

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© 1998 Springer-Verlag Berlin Heidelberg

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Mondelli, G., Castellano, G., Attolico, G., Distante, C. (1998). Parallel genetic evolution of membership functions and rules for a fuzzy controller. In: Sloot, P., Bubak, M., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1998. Lecture Notes in Computer Science, vol 1401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037234

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  • DOI: https://doi.org/10.1007/BFb0037234

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64443-9

  • Online ISBN: 978-3-540-69783-1

  • eBook Packages: Springer Book Archive

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