A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems

International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems

Volumn 15, Issue 5, 2007, Pages 539-557

  Alcala R ^a   Gacto, M J ^a   Herrera, F ^a   Alcala Fdez J ^b  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

Author keywords

Interpretability accuracy trade off; Linguistic modelling; Multi objective genetic algorithms; Rule selection; Tuning of membership functions

Indexed keywords

COMPUTATIONAL LINGUISTICS; DATA STRUCTURES; GENETIC ALGORITHMS; KNOWLEDGE BASED SYSTEMS; MULTIOBJECTIVE OPTIMIZATION; PARETO PRINCIPLE;

INTERPRETABILITY-ACCURACY TRADE-OFFS; LINGUISTIC MODELING; MULTIOBJECTIVE GENETIC ALGORITHMS; RULE SELECTION; TUNING OF MEMBERSHIP FUNCTIONS;

FUZZY SYSTEMS;

EID: 35348831258     PISSN: 02184885     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218488507004868     Document Type: Conference Paper

References (49)

1. J. Casillas, O. Cordón, F. Herrera and L. Magdalena (Eds.), Accuracy improvements in linguistic fuzzy modelling, Studies in Fuzziness and Soft Computing 129 (Springer, Heidelberg, Germany, 2003).
2. R. Alcalá, J. Alcalá-Fdez, J. Casillas, O. Cordón and F. Herrera, Hybrid learning models to get the interpretability-accuracy trade-off in fuzzy modeling, Soft Computing 10:9 (2006) 717-734.
3. R. Alcalá,, J. Alcalá-Fdez and F. Herrera,, A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection, IEEE Transactions on Fuzzy Systems (2007), in press.
4. R. Alcalá, J. Alcalá-Fdez, M. J. Gacto and F. Herrera, Rule base reduction and genetic tuning of fuzzy systems based on the linguistic 3-tuples representation, Soft Computing 11:5 (2007) 401-419.
5. J. Casillas, O. Cordón, M. J. del Jesus and F. Herrera, Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction, IEEE Trans, on Fuzzy Systems 13:1 (2005) 13-29.
6. O. Cordón and F. Herrera, A three-stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples, International Journal of Approximate Reasoning 17:4 (1997) 369-407.
7. H. B. Gürocak, A genetic-algorithm-based method for tuning fuzzy-logic controllers, Fuzzy Sets and Systems 108:1 (1999) 39-47.
8. 3 fuzzy rule systems from data using evolution strategies, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 29:4 (1999) 829-845.
9. B. D. Liu, C. Y. Chen and J. Y. Tsao, Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 31:1 (2001) 32-53.
10. D. Nauck and R. Kruse, Neuro-fuzzy systems for function approximation, Fuzzy Sets and Systems 101:2 (1999) 261-271.
11. C. A. Coello, D. A. Van Veldhuizen and G. B. Lamont, Evolutionary algorithms for solving multi-objective problems (Kluwer Academic Publishers, New York, 2002).
12. C. Coello, Recent trends in evolutionary multiobjective optimization, in A. Abraham, L. Jain, R. Goldberg (Ed.), Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Springer-Verlag, London, 2005) 7-32.
13. C. Coello, G. Toscano and E. Mezura, Current and future research trends in evolutionary multiobjective optimization, in: M. Grana, R. Duro, A. d'Anjou and P. P. Wang (Eds.), Information Processing with Evolutionary Algorithms: From Industrial Applications to Academic Speculations (Springer-Verlag, London, 2005) 213-231.
14. C. Coello, Evolutionary multiobjective optimization: A historical view of the field, IEEE Computational Intelligence Magazine 1:1 (2006) 28-36.
15. E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation 3:4 (1999) 257-271.
16. O. Cordon, F. Herrera, M. J. del Jesus and P. Villar, A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems, Proceedings of IX IFSA World Congress and XX NAFIPS International Conference (Vancouver, Canada, 2001) 1253-1258.
17. H. Ishibuchi, T. Murata and I. B. Turksen, Selecting linguistic classification rules by two-objective genetic algorithms, Proceedings of IEEE-SMC'95 (Vancouver, Canada, 1995) 1410-1415.
18. H. Ishibuchi and T. Murata, Minimizing the fuzzy rule base and its performance by a multi-objective genetic algorithm, Proceedings of 6th FUZZ-IEEE (Barcelona,, Spain, 1997) 259-264.
19. H. Ishibuchi, T. Murata, and M. Gen, Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms, Computers & Industrial Engineering 35:3-4 (1998) 575-578.
20. H. Ishibuchi, T. Nakashima and T. Murata, Three-objective genetics-based machine learning for linguistic rule extraction, Information Sciences 136 (2001) 109-133.
21. H. Ishibuchi, T. Nakashima and T. Murata, Multiobjective Optimization in Linguistic Rule Extraction from Numerical, Proceedings of First International Conference on Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science 1993 (Zurich, Switzerland, 2001) 588-602.
22. H. Ishibuchi and T. Yamamoto, Interpretability Issues in Fuzzy Genetics-Based Machine Learning for Linguistic Modelling, in: J. Lawry, J. G. Shanahan and A. L. Ralescu (Eds.), Modelling with Words: Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework, Lecture Notes in Computer Science 2873 (Springer-Verlag, Berlin, Heidelberg, 2003) 209-228.
23. H. Ishibuchi and T. Yamamoto, Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining, Fuzzy Sets and Systems 141:1 (2004) 59-88.
24. H. Ishibuchi and Y. Nojima, Accuracy-complexity tradeoff analysis by multiobjective rule selection, Proceedings of ICDM 2005 Workshop on Computational Intelligence in Data Mining (Houston, Texas, 2005) 39-48.
25. K. Narukawa, Y. Nojima and H. Ishibuchi, Modification of evolutionary multiobjective optimization algorithms for multiobjective design of fuzzy rule-based classification systems, Proceedings of 2005 IEEE International Conference on Fuzzy Systems (Reno, USA, 2005) 809-814.
26. E. Zitzler, M. Laumanns and L. Thiele, SPEA2: Improving the strength pareto evolutionary algorithm for multiObjetive optimization, Proc. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Barcelona, Spain, 2001, 95-100.
27. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans, on Evolutionary Computation 6:2 (2002) 182-197.
28. E. H. Mamdani, Applications of fuzzy algorithms for control a simple dynamic plant, Proceedings of the IEEE 121:12 (1974) 1585-1588.
29. E. H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies 7 (1975) 1-13.
30. T. Takagi and M. Sugeno, Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics 15 (1985) 116-132.
31. J. Casillas, O. Cordón, F. Herrera and L. Magdalena (Eds.), Interpretability issues in fuzzy modeling, Studies in Fuzziness and Soft Computing 128 (Springer, Heidelberg, Germany, 2003).
32. Y. Shi, R. Eberhart and Y. Chen, Implementation of evolutionary fuzzy systems, IEEE Transactions on Fuzzy Systems 7:2 (1999) 109-119.
33. R. Alcalá, J. Alcalá-Fdez, F. Herrera, and J. Otero, Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation, International Journal of Approximate Reasoning 44:1 (2007) 45-64.
34. W. Pedrycz, R. R. Gudwin and F. A. C. Gomide, Nonlinear context adaptation in the calibration of fuzzy sets, Fuzzy Sets and Systems 88:1 (1997) 91-97.
35. J. Espinosa, and J. Vandewalle, Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm, IEEE Transactions on Fuzzy Systems 8:5 (2000) 591-600.
36. O. Cordón, M. J. del Jesus and F. Herrera, Genetic learning of fuzzy rule-based classification systems cooperating with fuzzy reasoning methods, International Journal of Intelligent Systems 13 (1998) 1025-1053.
37. A. González and R. Pérez, A study about the inclusion of linguistic hedges in a fuzzy rule learning algorithm, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 7:3 (1999) 257-266.
38. R. Alcalá, J. R. Cano, O. Cordón, F. Herrera, P. Villar and I. Zwir, Linguistic modeling with hierarchical systems of weighted linguistic rules, International Journal of Approximate Reasoning 32:2-3 (2003) 187-215.
39. J. S. Cho and D. J. Park, Novel fuzzy logic control based on weighting of partially inconsistent rules using neural network, Journal of Intelligent and Fuzzy Systems 8 (2000) 99-110.
40. N. R. Pal and K. Pal, Handling of inconsistent rules with an extended model of fuzzy reasoning, Journal of Intelligent and Fuzzy Systems 7 (1999) 55-73.
41. T. P. Hong and C. Y. Lee, Effect of merging order on performance of fuzzy induction, Intelligent Data Analysis 3:2 (1999) 139-151.
42. H. Ishibuchi, K. Nozaki, N. Yamamoto and H. Tanaka, Selecting fuzzy if-then rules for classification problems using genetic algorithms, IEEE Transactions on Fuzzy Systems 3:3 (1995) 260-270.
43. T. P. Hong and J. B. Chen, Finding relevant attributes and membership functions, Fuzzy Sets and Systems 103:3 (1999) 389-404.
44. H. M. Lee, C. M. Chen, J. M. Chen and Y. L. Jou, An efficient fuzzy classifier with feature selection based on fuzzy entropy, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 31:3 (2001) 426-432.
45. L. J. Eshelman and J. D. Schaffer, Real-coded genetic algorithms and intervalschemata,, Foundations of Genetic Algorithms 2 (1993) 187-202.
46. L. J. Eshelman, The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination, Foundations of Genetic Algorithms 1 (1991) 265-283.
47. L. X. Wang and J. M. Mendel, Generating fuzzy rules by learning from examples, IEEE Trans. on Systems, Man, and Cybernetics 22:6 (1992) 1414-1427.
48. O. Cordón, F. Herrera, and L. Sánchez, Computing the Spanish Medium Electrical Line Maintenance Costs by means of Evolution-Based Learning Processes, Proc. International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, Castellón, Spain, 1998, 478-486.
49. O. Cordón, F. Herrera and L. Sánchez, Solving electrical distribution problems using hybrid evolutionary data, analysis techniques, Applied Intelligence 10 (1999) 5-24.