Genetic design of fuzzy knowledge bases - a study of different approaches

The objective of this work is to design, implement and test two different genetic fuzzy systems approaches with the purpose of analyzing the performance of both when applied to classification problems. In the first approach the fuzzy sets are defined previously by fuzzy clustering and the rule base is automatically generated and optimized using genetic algorithms. In the second approach the data base is the object of genetic algorithm learning, instead of the rule base. In this case, the rule base is generated by means of an auxiliary method (Wang & Mendell). Investigations of both methods developed earlier by the authors are described and then, the results of the comparison experiments performed in the present work are presented. The methods have been selected for investigation with the objective of analyzing the performance and the size of the resulting knowledge bases generated through genetic algorithms applied to different KB components.

[1]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[2]  Isao Hayashi,et al.  A learning method of fuzzy inference rules by descent method , 1992 .

[3]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

[4]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[5]  Francisco Herrera,et al.  Tuning fuzzy logic controllers by genetic algorithms , 1995, Int. J. Approx. Reason..

[6]  Yufei Yuan,et al.  A genetic algorithm for generating fuzzy classification rules , 1996, Fuzzy Sets Syst..

[7]  Dimitar P. Filev,et al.  Fuzzy SETS AND FUZZY LOGIC , 1996 .

[8]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[9]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[10]  Hisao Ishibuchi,et al.  Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems , 1997, Fuzzy Sets Syst..

[11]  Beloslav Riečan,et al.  Sets and fuzzy sets , 1997 .

[12]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[13]  Rudolf Kruse,et al.  A neuro-fuzzy method to learn fuzzy classification rules from data , 1997, Fuzzy Sets Syst..

[14]  Francisco Herrera,et al.  A learning process for fuzzy control rules using genetic algorithms , 1998, Fuzzy Sets Syst..

[15]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[16]  Palma Blonda,et al.  A survey of fuzzy clustering algorithms for pattern recognition. I , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[17]  F. Herrera,et al.  A proposal on reasoning methods in fuzzy rule-based classification systems , 1999 .

[18]  F. Gomide,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[19]  Francisco Herrera,et al.  Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems , 2001, Fuzzy Sets Syst..

[20]  Francisco Herrera,et al.  Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base , 2001, IEEE Trans. Fuzzy Syst..

[21]  Francisco Herrera,et al.  Recent advances in genetic fuzzy systems - Guest editorial , 2001, Inf. Sci..

[22]  Uzay Kaymak,et al.  Fuzzy classification using probability-based rule weighting , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[23]  T. Warren Liao,et al.  II, A fuzzy c-means variant for the generation of fuzzy term sets , 2003, Fuzzy Sets Syst..

[24]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[25]  Heloisa A. Camargo,et al.  Learning and optimization of fuzzy rule base by means of self-adaptive genetic algorithm , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).