Abstract
GEFREX is a genetic-neuro-fuzzy tool able to extract very compact fuzzy rules. It is mainly based on the genetic paradigm. This paper shows the preliminary result obtained by the author regarding the parallelization of this tool. The hw platform consists of a cluster of 7 PC-based single pentium II 233 MHz workstations and 1 PC-based double pentium II 300MHz processor server. The communication network is a fast-ethernet dedicated LAN. The results achieved are very promising. With up to seven processing elements, the speedup obtainable is linear.
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References
E. Cantú-Paz, “A Survey on Parallel Genetic Algorithms”, Tech. Rep. 97003, Illinois Genetics Algorithms Laboratory, 104 S. Mathews Avenue Urbana, IL 61801, May 1997.
H. Chen, N.S. Flann, and D.W. Watson, “Parallel genetic simulated annealing: A massively parallel simd algorithm”, IEEE Transactions on Parallel and Distributed Systems, vol. 9, pp. 126–136, Feb. 1998.
M. Russo, Hybrid Learning for Fuzzy Systems, vol. 41, pp. 440–445. Amsterdam: IOS Press, 1997.
M. Russo, “GEFREX: A GEnetic Fuzzy Rule EXtractor”, International Journal of Knowledge based Intelligent Engineering Systems, vol. 2, pp. 49–59, Jan. 1998.
F.J. Marin, O. Trelles-Salazar, and F. Sandoval, Parallel Problem Solving from Nature, PPSN III, ch. Genetic algorithms on LAN-message passing architectures using PVM: Application to the routing problem, pp. 534–543. Berlin: Springer-Verlag, 1994.
M. Russo, “FuGeNeSys: A Genetic Neural System for Fuzzy Modeling”, IEEE Transactions on Fuzzy Systems, vol. 6, pp. 373–388, Aug. 1998.
M. Russo, Metodi hardware e Software per Logiche di Tipo non Tradizionale. PhD thesis, University of Catania, Catania, Italy, Feb. 1996.
G.H. Golub and C.F. Van Loan, Matrix Computations. Baltimore and London: The Johns Hopkins Iniversity Press, 1996.
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Russo, M. (1999). Parallel fuzzy learning. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098222
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DOI: https://doi.org/10.1007/BFb0098222
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