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Vol: 59(73) No: 1 / June 2014 Generating Inference Rules for Semantic Analysis Using Genetic Algorithms and a Novel Chromosome Type Norbert Gal Politehnica University of Timisoara, Department of Automation and Applied Informatics, Bd. V. Parvan, 300223 Timisoara, Romania, e-mail: norbert.gal@upt.ro Vasile Stoicu-Tivadar Politehnica University of Timisoara, Department of Automation and Applied Informatics, Bd. V. Parvan, 300223 Timisoara, Romania, e-mail: vasile.stoicu-tivadar@upt.ro Keywords: genetic algorithms, fuzzy inference systems, rule optimization Abstract This paper presents a novel chromosome and a new method for generating fuzzy inference rules. The paper presents a new method of coding the fuzzy rules into the chromosomes, by using the linguistic values of the fuzzy sets and linguistic variables. A new fitness function and a new crossover method are described as well. Conclusions are issued. References [1] Y. F. Li and C. C. Lan, “Development of fuzzy algorithms for servo systems,” IEEE Contr. Syst. Mag., vol. 9, no. 3, pp. 65- 73, 1989. [2] W. Pedrycz, Fuzzy Control and Fuzzy Systems. New York: J. Wiley, 1989. [3] C. H. Lee and C. C. Teng, “Identification and control of dynamic systems using recurrent fuzzy neural networks,” IEEE Trans. Fuzzy Syst., vol. 8, no. 4, pp. 349-366, Aug. 2000. [4] T. Yamakawa and T. Koga, “Bio-inspired self-organizing relationship network as knowledge acquisition tool and fuzzy inference engine,” in Proc. IEEE World Conf. Comput. Intell., 2008, pp. 159-180. [5] N. Gal and V. Stoicu-Tivadar, “Knowledge representation for fuzzy inference aided medical image interpretation”, in Proc. 24th European Medical Informatics Conference (MIE2012), Pisa, Italy, 2012, pp. 98-102. [6] G. Renner and A. Ekárt, “Genetic algorithms in computer aided design,” Computer-Aided Design, vol. 35, no. 8, 2003, pp. 709-726. [7] A. H. M. Pimenta and H. A. Camargo, “Interval type-2 fuzzy classifier design using genetic algorithms,” in Proc. 2010 IEEE International Conference on Fuzzy Systems, 2010, pp. 1-7. [8] P. C. Shill, K. K Pal, M. F. Amin, and K. Murase, “Genetic algorithm based fully automated and adaptive fuzzy logic controller,” in Proc. 2011 IEEE International Conference on Fuzzy Systems, 2011, pp. 1572-1579. [9] N. Gal and V. Stoicu-Tivadar, “Using genetic algorithms to generate inference rules for semantic analysis of medical images,” in Proc. 32nd National Conference on Medical Informatics (RO-MEDINF 2012), Timisoara, Romania, 2012, pp. 1-4. [10] R.-E. Precup and S. Preitl, Fuzzy Controllers. Timisoara: Editura Orizonturi Universitare, 1999. |