Parallel genetic evolution of membership functions and rules for a fuzzy controller
Year: 1998
Authors: Mondelli G., Castellano G., Attolico G., Distante C.
Autors Affiliation: CNR, Ist Elaboraz Segnali & Immagini, Via Amendola 166-5, I-70126 Bari, Italy
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.
Journal/Review: LECTURE NOTES IN COMPUTER SCIENCE
Volume: 1401 Pages from: 922 to: 924
More Information: Presentato a : International Conference and Exhibition on High-Performance Computing and Networking, AMSTERDAM, NETHERLANDS – APR 21-23, 1998KeyWords: Parallel Genetic AlgorithmsCitations: 2data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-09-29References taken from IsiWeb of Knowledge: (subscribers only)Connecting to view paper tab on IsiWeb: Click hereConnecting to view citations from IsiWeb: Click here