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Proceedings Paper

Fuzzy controller design by parallel genetic algorithms
Author(s): G. Mondelli; G. Castellano; Giovanni Attolico; Arcangelo Distante
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Paper Abstract

Designing a fuzzy system involves defining membership functions and constructing rules. Carrying out these two steps manually often results in a poorly performing system. Genetic Algorithms (GAs) has proved to be a useful tool for designing optimal fuzzy controller. In order to increase the efficiency and effectiveness of their application, parallel GAs (PAGs), evolving synchronously several populations with different balances between exploration and exploitation, have been implemented using a SIMD machine (APE100/Quadrics). The parameters to be identified are coded in such a way that the algorithm implicitly provides a compact fuzzy controller, by finding only necessary rules and removing useless inputs from them. Early results, working on a fuzzy controller implementing the wall-following task for a real vehicle as a test case, provided better fitness values in less generations with respect to previous experiments made using a sequential implementation of GAs.

Paper Details

Date Published: 25 March 1998
PDF: 10 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304823
Show Author Affiliations
G. Mondelli, Istituto per l'Elaborazione di Segnali ed Immagini (Italy)
G. Castellano, Istituto per l'Elaborazione di Segnali ed Immagini (Italy)
Giovanni Attolico, Istituto per l'Elaborazione di Segnali ed Immagini (Italy)
Arcangelo Distante, Istituto per l'Elaborazione di Segnali ed Immagini (Italy)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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