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Published in: Evolutionary Intelligence 1/2008

01-03-2008 | Review Article

Genetic fuzzy systems: taxonomy, current research trends and prospects

Author: Francisco Herrera

Published in: Evolutionary Intelligence | Issue 1/2008

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Abstract

The use of genetic algorithms for designing fuzzy systems provides them with the learning and adaptation capabilities and is called genetic fuzzy systems (GFSs). This topic has attracted considerable attention in the Computation Intelligence community in the last few years. This paper gives an overview of the field of GFSs, being organized in the following four parts: (a) a taxonomy proposal focused on the fuzzy system components involved in the genetic learning process; (b) a quick snapshot of the GFSs status paying attention to the pioneer GFSs contributions, showing the GFSs visibility at ISI Web of Science including the most cited papers and pointing out the milestones covered by the books and the special issues in the topic; (c) the current research lines together with a discussion on critical considerations of the recent developments; and (d) some potential future research directions.

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Literature
1.
go back to reference Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6:443–462CrossRef Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6:443–462CrossRef
2.
go back to reference Alcalá R, Casillas J, Cordón O, Herrera F (2001) Building fuzzy graphs: features and taxonomy of learning non-grid-oriented fuzzy rule-based systems. Int J Intell Fuzzy Syst 11:99–119 Alcalá R, Casillas J, Cordón O, Herrera F (2001) Building fuzzy graphs: features and taxonomy of learning non-grid-oriented fuzzy rule-based systems. Int J Intell Fuzzy Syst 11:99–119
3.
go back to reference Alcalá R, Gacto MJ, Herrera F, Alcalá-Fdez J (2007a) A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems. Int J Uncertain Fuzziness Knowl Based Syst 15(5):521–537CrossRef Alcalá R, Gacto MJ, Herrera F, Alcalá-Fdez J (2007a) A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems. Int J Uncertain Fuzziness Knowl Based Syst 15(5):521–537CrossRef
4.
go back to reference Alcalá R, Alcalá-Fdez R, Herrera F, Otero J (2007b) Genetic learning of accurate and compact fuzzy rule based systems based on the 2-Tuples linguistic representation. Int J Approx Reason 44:45–64MATHCrossRef Alcalá R, Alcalá-Fdez R, Herrera F, Otero J (2007b) Genetic learning of accurate and compact fuzzy rule based systems based on the 2-Tuples linguistic representation. Int J Approx Reason 44:45–64MATHCrossRef
5.
go back to reference Alcalá R, Alcalá-Fdez J, Gacto MJ, Herrera F (2008) A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems. Int J Uncertain Fuzziness Knowl Based Syst (in press) Alcalá R, Alcalá-Fdez J, Gacto MJ, Herrera F (2008) A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems. Int J Uncertain Fuzziness Knowl Based Syst (in press)
6.
go back to reference Alcalá-Fdez J, Herrera F, Marquez F, Peregrin A (2007) Increasing fuzzy rules cooperation based on evolutionary adaptive inference systems. Int J Intell Syst 22(9):1035–1064MATHCrossRef Alcalá-Fdez J, Herrera F, Marquez F, Peregrin A (2007) Increasing fuzzy rules cooperation based on evolutionary adaptive inference systems. Int J Intell Syst 22(9):1035–1064MATHCrossRef
7.
go back to reference Alcalá-Fdez J, Sánchez L, García S, del Jesús MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2008) KEEL: A software tool to assess evolutionary algorithms for data mining problems. Soft Comput (in press) Alcalá-Fdez J, Sánchez L, García S, del Jesús MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2008) KEEL: A software tool to assess evolutionary algorithms for data mining problems. Soft Comput (in press)
8.
go back to reference Au W-H, Chan KCC, Wong AKC (2006) A fuzzy approach to partitioning continuous attributes for classification. IEEE Trans Knowl Data Eng 18(5):715–719CrossRef Au W-H, Chan KCC, Wong AKC (2006) A fuzzy approach to partitioning continuous attributes for classification. IEEE Trans Knowl Data Eng 18(5):715–719CrossRef
9.
go back to reference Berlanga FJ, del Jesus MJ, González P, Herrera F, Mesonero M (2006) Multiobjective evolutionary induction of subgroup discovery fuzzy rules: a case study in marketing. In: 6th industrial conference on data mining (ICDM 2006), Leipzig, Germany, Lecture Notes in Computer Science 4065, Springer, Heidelberg, pp 337–349 Berlanga FJ, del Jesus MJ, González P, Herrera F, Mesonero M (2006) Multiobjective evolutionary induction of subgroup discovery fuzzy rules: a case study in marketing. In: 6th industrial conference on data mining (ICDM 2006), Leipzig, Germany, Lecture Notes in Computer Science 4065, Springer, Heidelberg, pp 337–349
10.
go back to reference Bernadó-Mansilla E, Garrell-Guiu JM (2003) Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. Evol Comput 11(3):209–238CrossRef Bernadó-Mansilla E, Garrell-Guiu JM (2003) Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. Evol Comput 11(3):209–238CrossRef
11.
go back to reference Botta A, Lazzerini B and Marcelloni F (2006) Context adaptation of Mamdani fuzzy systems through new operators tuned by a genetic algorithm. In Proceedings of the 2006 IEEE international conference on fuzzy systems (FUZZ-IEEE’06), Vancouver, Canada, pp 7832–7839 Botta A, Lazzerini B and Marcelloni F (2006) Context adaptation of Mamdani fuzzy systems through new operators tuned by a genetic algorithm. In Proceedings of the 2006 IEEE international conference on fuzzy systems (FUZZ-IEEE’06), Vancouver, Canada, pp 7832–7839
12.
go back to reference Botta A, Lazzerini B, Marcelloni F and Stefanescu DC (2007) Exploiting fuzzy ordering relations to preserve interpretability in context adaptation of fuzzy systems. In: Proceedings of the 2007 IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 1137–1142 Botta A, Lazzerini B, Marcelloni F and Stefanescu DC (2007) Exploiting fuzzy ordering relations to preserve interpretability in context adaptation of fuzzy systems. In: Proceedings of the 2007 IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 1137–1142
13.
go back to reference Cano JR, Herrera F, Lozano M (2007) Evolutionary stratified training set selection for extracting classification rules with trade-off precision-interpretability. Data Knowl Eng 60:90–108CrossRef Cano JR, Herrera F, Lozano M (2007) Evolutionary stratified training set selection for extracting classification rules with trade-off precision-interpretability. Data Knowl Eng 60:90–108CrossRef
14.
go back to reference Carse B, Fogarty TC, Munro A (1996) Evolving fuzzy rule based controllers using genetic algorithms. Fuzzy Sets Syst 80(3):273–293CrossRef Carse B, Fogarty TC, Munro A (1996) Evolving fuzzy rule based controllers using genetic algorithms. Fuzzy Sets Syst 80(3):273–293CrossRef
15.
go back to reference Casillas J, Carse B, Bull L (2007) Fuzzy-XCS: A Michigan genetic fuzzy system. IEEE Trans Fuzzy Syst 15(4):536–550CrossRef Casillas J, Carse B, Bull L (2007) Fuzzy-XCS: A Michigan genetic fuzzy system. IEEE Trans Fuzzy Syst 15(4):536–550CrossRef
16.
go back to reference Casillas J, Cordón O, Herrera F, del Jesus MJ (2001) Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. Inf Sci 136(1–4):135–157MATHCrossRef Casillas J, Cordón O, Herrera F, del Jesus MJ (2001) Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. Inf Sci 136(1–4):135–157MATHCrossRef
17.
go back to reference Casillas J, Cordón O, del Jesus MJ, Herrera F (2005) Genetic tuning of fuzzy rule deep structures preserving interpretability for linguistic modeling. IEEE Trans Fuzzy Syst 13(1):13–29CrossRef Casillas J, Cordón O, del Jesus MJ, Herrera F (2005) Genetic tuning of fuzzy rule deep structures preserving interpretability for linguistic modeling. IEEE Trans Fuzzy Syst 13(1):13–29CrossRef
18.
go back to reference Casillas J, Cordón O, Herrera F, Magdalena L (Eds) (2003a) Accuracy improvements in linguistic fuzzy modelling. Springer, Berlin Casillas J, Cordón O, Herrera F, Magdalena L (Eds) (2003a) Accuracy improvements in linguistic fuzzy modelling. Springer, Berlin
19.
go back to reference Casillas J, Cordón O, Herrera F, Magdalena L (Eds) (2003b) Interpretability issues in fuzzy modelling. Springer, Berlin Casillas J, Cordón O, Herrera F, Magdalena L (Eds) (2003b) Interpretability issues in fuzzy modelling. Springer, Berlin
20.
go back to reference Casillas J, Martínez P (2007) Consistent, complete and compact generation of DNF-type fuzzy rules by a Pittsburgh-style genetic algorithm. In: Proceedings of the 2007 IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 1745–1750 Casillas J, Martínez P (2007) Consistent, complete and compact generation of DNF-type fuzzy rules by a Pittsburgh-style genetic algorithm. In: Proceedings of the 2007 IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 1745–1750
21.
go back to reference Cococcioni M, Ducange P, Lazzerini B, Marcelloni F (2007) A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems. Soft Comput 11(11):1013–1031CrossRef Cococcioni M, Ducange P, Lazzerini B, Marcelloni F (2007) A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems. Soft Comput 11(11):1013–1031CrossRef
22.
go back to reference Cherkassky V, Mulier F (1998) Learning from data: concepts, theory and methods. Wiley, New YorkMATH Cherkassky V, Mulier F (1998) Learning from data: concepts, theory and methods. Wiley, New YorkMATH
23.
go back to reference Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, Kluwer Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, Kluwer
24.
go back to reference Cordón O, del Jesús MJ, Herrera F, Lozano M (1999) MOGUL: a methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach. Int J Intell Syst 14:123–1153CrossRef Cordón O, del Jesús MJ, Herrera F, Lozano M (1999) MOGUL: a methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach. Int J Intell Syst 14:123–1153CrossRef
25.
go back to reference Cordón O, Gomide F, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 141:5–31MATHCrossRef Cordón O, Gomide F, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 141:5–31MATHCrossRef
26.
go back to reference Cordón O, Herrera F (1997) A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples. Int J Approx Reason 17(4):369–407MATHCrossRef Cordón O, Herrera F (1997) A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples. Int J Approx Reason 17(4):369–407MATHCrossRef
27.
go back to reference Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, SingaporeMATH Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, SingaporeMATH
28.
go back to reference Cordón O, Herrera F, Villar P (2000) Analysis and guidelines to obtain a good fuzzy partition granularity for fuzzy rule-based systems using simulated annealing. Int J Approx Reason 25(3):187–215MATHCrossRef Cordón O, Herrera F, Villar P (2000) Analysis and guidelines to obtain a good fuzzy partition granularity for fuzzy rule-based systems using simulated annealing. Int J Approx Reason 25(3):187–215MATHCrossRef
29.
go back to reference Cordón O, Herrera F, Magdalena L, Villar P (2001a) A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base. Inf Sci 136:85–107MATHCrossRef Cordón O, Herrera F, Magdalena L, Villar P (2001a) A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base. Inf Sci 136:85–107MATHCrossRef
30.
go back to reference Cordón O, Herrera F, Villar P (2001b) Generating the knowledge base of a fuzzy rule-based system by the genetic learning of data base. IEEE Trans Fuzzy Syst 9(4):667–674CrossRef Cordón O, Herrera F, Villar P (2001b) Generating the knowledge base of a fuzzy rule-based system by the genetic learning of data base. IEEE Trans Fuzzy Syst 9(4):667–674CrossRef
31.
go back to reference Crockett KA, Bandar Z, Fowdar J, O’Shea J (2006) Genetic tuning of fuzzy inference within fuzzy classifier systems. Expert Syst Appl 23:63–82CrossRef Crockett KA, Bandar Z, Fowdar J, O’Shea J (2006) Genetic tuning of fuzzy inference within fuzzy classifier systems. Expert Syst Appl 23:63–82CrossRef
32.
go back to reference Crockett K, Bandar Z, Mclean D (2007) On the optimization of T-norm parameters within fuzzy decision trees. In: IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 103–108 Crockett K, Bandar Z, Mclean D (2007) On the optimization of T-norm parameters within fuzzy decision trees. In: IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 103–108
33.
go back to reference Deb K (2001) Multi-objective optimization using evolutionary algorithms.Wiley, New York Deb K (2001) Multi-objective optimization using evolutionary algorithms.Wiley, New York
34.
go back to reference Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef
35.
go back to reference De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13:161–188CrossRef De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13:161–188CrossRef
36.
go back to reference del Jesus MJ, González P, Herrera F, Mesonero M (2007) Evolutionary fuzzy rule induction process for subgroup discovery: a case study in marketing. IEEE Trans Fuzzy Syst 15(4):578–592CrossRef del Jesus MJ, González P, Herrera F, Mesonero M (2007) Evolutionary fuzzy rule induction process for subgroup discovery: a case study in marketing. IEEE Trans Fuzzy Syst 15(4):578–592CrossRef
37.
go back to reference Demsar J (2006) Statistical comparison of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNet Demsar J (2006) Statistical comparison of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNet
38.
go back to reference Diettereich T (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10:1895–1924CrossRef Diettereich T (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10:1895–1924CrossRef
39.
go back to reference Dubois D, Prade H, Sudkamp T (2005) On the representation, measurement, and discovery of fuzzy associations. IEEE Trans Fuzzy Syst 13:250–262CrossRef Dubois D, Prade H, Sudkamp T (2005) On the representation, measurement, and discovery of fuzzy associations. IEEE Trans Fuzzy Syst 13:250–262CrossRef
40.
go back to reference Eiben AE, Smith JE (2003) Introduction to evolutionary computation. Springer, Berlin Eiben AE, Smith JE (2003) Introduction to evolutionary computation. Springer, Berlin
41.
go back to reference Fayyad U, Piatesky-Shapiro G, Smyth P (1996) From data mining from knowledge discovery in databases. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery & data mining, AAAI/MIT, pp 1–34 Fayyad U, Piatesky-Shapiro G, Smyth P (1996) From data mining from knowledge discovery in databases. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery & data mining, AAAI/MIT, pp 1–34
42.
go back to reference Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, BerlinMATH Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, BerlinMATH
43.
go back to reference Geyer-Schulz A (1995) Fuzzy rule-based expert systems and genetic machine learning. Physica-Verlag, Berlin Geyer-Schulz A (1995) Fuzzy rule-based expert systems and genetic machine learning. Physica-Verlag, Berlin
44.
go back to reference Giordana A, Neri F (1995) Search-intensive concept induction. Evol Comput 3:375–416 Giordana A, Neri F (1995) Search-intensive concept induction. Evol Comput 3:375–416
45.
go back to reference Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingMATH Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingMATH
46.
go back to reference González A, Pérez R (1999) SLAVE: A genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 27:176–191CrossRef González A, Pérez R (1999) SLAVE: A genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 27:176–191CrossRef
47.
go back to reference González A, Pérez R (2001) Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Trans Syst Man Cybern B Cybern 31(3):417–425CrossRef González A, Pérez R (2001) Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Trans Syst Man Cybern B Cybern 31(3):417–425CrossRef
48.
go back to reference González A, Pérez R (2006) An analysis of the scalability of an embedded feature selection model for classification problems. In: Proceedings of eleventh international conference on information processing and management of uncertainty in knowledge-based systems (IPMU’06), Paris, pp 1949–1956 González A, Pérez R (2006) An analysis of the scalability of an embedded feature selection model for classification problems. In: Proceedings of eleventh international conference on information processing and management of uncertainty in knowledge-based systems (IPMU’06), Paris, pp 1949–1956
49.
go back to reference Greene DP, Smith SF (1993) Competition-based induction of decision models from examples. Mach Learn 3:229–257CrossRef Greene DP, Smith SF (1993) Competition-based induction of decision models from examples. Mach Learn 3:229–257CrossRef
50.
go back to reference Gudwin RR, Gomide FAC, Pedrycz W (1998) Context adaptation in fuzzy processing and genetic algorithms. Int J Intell Syst 13(10–11):929–948CrossRef Gudwin RR, Gomide FAC, Pedrycz W (1998) Context adaptation in fuzzy processing and genetic algorithms. Int J Intell Syst 13(10–11):929–948CrossRef
51.
go back to reference Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Mining Knowl Discov 15(1):55–86CrossRefMathSciNet Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Mining Knowl Discov 15(1):55–86CrossRefMathSciNet
52.
go back to reference Herrera F (2005) Genetic fuzzy systems: Status, critical considerations and future directions. Int J Comput Intell Res 1(1):59–67 Herrera F (2005) Genetic fuzzy systems: Status, critical considerations and future directions. Int J Comput Intell Res 1(1):59–67
53.
go back to reference Herrera F, Lozano M, Verdegay JL (1995) Tuning fuzzy-logic controllers by genetic algorithms. Int J Approx Reason 12(3–4):299–315MATHCrossRefMathSciNet Herrera F, Lozano M, Verdegay JL (1995) Tuning fuzzy-logic controllers by genetic algorithms. Int J Approx Reason 12(3–4):299–315MATHCrossRefMathSciNet
54.
go back to reference Herrera F, Lozano M, Verdegay JL (1998) A learning process for fuzzy control rules using genetic algorithms. Fuzzy Sets Syst 100:143–151CrossRef Herrera F, Lozano M, Verdegay JL (1998) A learning process for fuzzy control rules using genetic algorithms. Fuzzy Sets Syst 100:143–151CrossRef
55.
go back to reference Homaifar A, Mccormick E (1995) Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans Fuzzy Syst 3(2):129–139CrossRef Homaifar A, Mccormick E (1995) Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans Fuzzy Syst 3(2):129–139CrossRef
56.
go back to reference Hoffmann F, Schauten D, Hölemann S (2007) Incremental evolutionary design of TSK fuzzy controllers. IEEE Trans Fuzzy Syst 15(4):563–577CrossRef Hoffmann F, Schauten D, Hölemann S (2007) Incremental evolutionary design of TSK fuzzy controllers. IEEE Trans Fuzzy Syst 15(4):563–577CrossRef
57.
go back to reference Holland JH (1975) Adaptatioon in natural and artificial systems. University of Michigan Press, Ann Arbor Holland JH (1975) Adaptatioon in natural and artificial systems. University of Michigan Press, Ann Arbor
58.
go back to reference Holland JH, Reitman JS (1978) Cognitive systems based on adaptive algorithms. In: Waterman DA, Hayes-Roth F (eds) Patter-directed inference systems. Academic Press, London Holland JH, Reitman JS (1978) Cognitive systems based on adaptive algorithms. In: Waterman DA, Hayes-Roth F (eds) Patter-directed inference systems. Academic Press, London
59.
go back to reference Hong TP, Chen CH, Wu YL et al (2006) A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Comput 10(11):1091–1101CrossRef Hong TP, Chen CH, Wu YL et al (2006) A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Comput 10(11):1091–1101CrossRef
60.
go back to reference Hüllermeier E (2005) Fuzzy methods in machine learning and data mining: Status and prospects. Fuzzy Sets Syst 156(3):387–406CrossRef Hüllermeier E (2005) Fuzzy methods in machine learning and data mining: Status and prospects. Fuzzy Sets Syst 156(3):387–406CrossRef
61.
go back to reference Ishibuchi H (2007) Multiobjective genetic fuzzy systems: review and future research directions. In: Proceedings of the 2007 IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 913–918 Ishibuchi H (2007) Multiobjective genetic fuzzy systems: review and future research directions. In: Proceedings of the 2007 IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 913–918
62.
go back to reference Ishibuchi H, Murata T, Turksen IB (1997) Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst 8(2):135–150CrossRef Ishibuchi H, Murata T, Turksen IB (1997) Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst 8(2):135–150CrossRef
63.
go back to reference Ishibuchi H, Nakashima T, Murata T (1999) Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans Syst Man Cybern B Cybern 29(5):601–618CrossRef Ishibuchi H, Nakashima T, Murata T (1999) Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans Syst Man Cybern B Cybern 29(5):601–618CrossRef
64.
go back to reference Ishibuchi H, Nakashima T, Nii M (2004) Classification and modeling with linguistic information granules: advanced approaches to linguistic data mining. Springer, Berlin Ishibuchi H, Nakashima T, Nii M (2004) Classification and modeling with linguistic information granules: advanced approaches to linguistic data mining. Springer, Berlin
65.
go back to reference Ishibuchi H, Nozaki K, Yamamoto N, Tanaka H (1995) Selection fuzzy IF-THEN rules for classification problems using genetic algorithms. IEEE Trans Fuzzy Syst 3(3): 260–270CrossRef Ishibuchi H, Nozaki K, Yamamoto N, Tanaka H (1995) Selection fuzzy IF-THEN rules for classification problems using genetic algorithms. IEEE Trans Fuzzy Syst 3(3): 260–270CrossRef
66.
go back to reference Ishibuchi H, Yamamoto T (2004) Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst 141(1):59–88MATHCrossRefMathSciNet Ishibuchi H, Yamamoto T (2004) Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst 141(1):59–88MATHCrossRefMathSciNet
67.
go back to reference Juang CF, Lin JY, Lin CT (2000) Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Trans Syst Man Cybern B Cybern 30(2):290–302CrossRefMathSciNet Juang CF, Lin JY, Lin CT (2000) Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Trans Syst Man Cybern B Cybern 30(2):290–302CrossRefMathSciNet
68.
go back to reference Karr C (1991) Genetic algorithms for fuzzy controllers. AI Expert 6(2):26–33 Karr C (1991) Genetic algorithms for fuzzy controllers. AI Expert 6(2):26–33
69.
go back to reference Kaya M (2006) Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules. Soft Comput 10(7): 578–586MATHCrossRefMathSciNet Kaya M (2006) Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules. Soft Comput 10(7): 578–586MATHCrossRefMathSciNet
70.
71.
go back to reference Kim D, Choi Y, Lee S (2002) An accurate COG defuzzifier design using Lamarckian co-adaptation of learning and evolution. Fuzzy Sets Syst 130(2):207–225MATHCrossRefMathSciNet Kim D, Choi Y, Lee S (2002) An accurate COG defuzzifier design using Lamarckian co-adaptation of learning and evolution. Fuzzy Sets Syst 130(2):207–225MATHCrossRefMathSciNet
72.
go back to reference Klösgen W (1996) EXPLORA: a multipattern and multistrategy discovery assistant. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining, MIT Press, New York, pp 249–271 Klösgen W (1996) EXPLORA: a multipattern and multistrategy discovery assistant. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining, MIT Press, New York, pp 249–271
73.
go back to reference Konar A (2005) Computational Intelligence: Principles, techniques and applications. Springer, BerlinMATH Konar A (2005) Computational Intelligence: Principles, techniques and applications. Springer, BerlinMATH
74.
go back to reference Kovacs T (2004) Strength or accuracy: credit assignment in learning classifier systems. Springer, BerlinMATH Kovacs T (2004) Strength or accuracy: credit assignment in learning classifier systems. Springer, BerlinMATH
75.
go back to reference Kuncheva L (2000) Fuzzy classifier design. Springer, BerlinMATH Kuncheva L (2000) Fuzzy classifier design. Springer, BerlinMATH
76.
go back to reference Kweku-Muata, Osey-Bryson (2004) Evaluation of decision trees: a multicriteria approach. Comput Oper Res 31:1933–1945 Kweku-Muata, Osey-Bryson (2004) Evaluation of decision trees: a multicriteria approach. Comput Oper Res 31:1933–1945
77.
go back to reference Lavrač N, Cestnik B, Gamberger D, Flach P (2004) Decision support through subgroup discovery: three case studies and the lessons learned. Mach Learn 57:115–143CrossRefMATH Lavrač N, Cestnik B, Gamberger D, Flach P (2004) Decision support through subgroup discovery: three case studies and the lessons learned. Mach Learn 57:115–143CrossRefMATH
78.
go back to reference Magdalena L (1997) Adapting the gain of an FLC with genetic algorithms. Int J Approx Reason 17(4):327–349MATHCrossRef Magdalena L (1997) Adapting the gain of an FLC with genetic algorithms. Int J Approx Reason 17(4):327–349MATHCrossRef
79.
go back to reference Mamdani EH (1974) Applications of fuzzy algorithm for control a simple dynamic plant. Proc IEEE 121(12):1585–1588 Mamdani EH (1974) Applications of fuzzy algorithm for control a simple dynamic plant. Proc IEEE 121(12):1585–1588
80.
go back to reference Márquez FA, Peregrín A, Herrera F (2007) Cooperative evolutionary learning of linguistic fuzzy rules and parametric aggregation connectors for Mamdani fuzzy systems. IEEE Trans Fuzzy Syst 15(6):1168–1178CrossRef Márquez FA, Peregrín A, Herrera F (2007) Cooperative evolutionary learning of linguistic fuzzy rules and parametric aggregation connectors for Mamdani fuzzy systems. IEEE Trans Fuzzy Syst 15(6):1168–1178CrossRef
81.
go back to reference Mikut R, Jäkel J, Gröll L (2005) Interpretability issues in data-based learning of fuzzy systems. Fuzzy Sets Syst 150:179–197MATHCrossRef Mikut R, Jäkel J, Gröll L (2005) Interpretability issues in data-based learning of fuzzy systems. Fuzzy Sets Syst 150:179–197MATHCrossRef
82.
go back to reference Moriarty DE, Miikkulainen R (1996) Efficient reinforcement learning through symbiotic evolution. Mach Learn 22:11–32 Moriarty DE, Miikkulainen R (1996) Efficient reinforcement learning through symbiotic evolution. Mach Learn 22:11–32
83.
go back to reference Nojima Y, Kuwajima I, Ishibuchi H (2007) Data set subdivision for parallel distribution implementation of genetic fuzzy rule selection. In: IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 2006–2011 Nojima Y, Kuwajima I, Ishibuchi H (2007) Data set subdivision for parallel distribution implementation of genetic fuzzy rule selection. In: IEEE international conference on fuzzy systems (FUZZ-IEEE’07), London, pp 2006–2011
84.
go back to reference Orriols-Puig A, Casillas J, Bernadó-Mansilla E (2007) Fuzzy-UCS: preliminary results. In: 10th international workshop on learning classifier systems (IWLCS 2007), London, pp 2871–2874 Orriols-Puig A, Casillas J, Bernadó-Mansilla E (2007) Fuzzy-UCS: preliminary results. In: 10th international workshop on learning classifier systems (IWLCS 2007), London, pp 2871–2874
85.
go back to reference Palm R, Driankov D, Hellendoorn (1997) Model based fuzzy control. Springer, BerlinMATH Palm R, Driankov D, Hellendoorn (1997) Model based fuzzy control. Springer, BerlinMATH
86.
go back to reference Park D, Kandel A, Langholz G (1994) Genetic-based new fuzzy-reasoning models with applications to fuzzy control. IEEE Trans Syst Man Cybern 24(1):39–47CrossRef Park D, Kandel A, Langholz G (1994) Genetic-based new fuzzy-reasoning models with applications to fuzzy control. IEEE Trans Syst Man Cybern 24(1):39–47CrossRef
87.
go back to reference Pedrycz W (Ed.) (1996) Fuzzy modelling: Paradigms and practice. Kluwer, Dordrecht Pedrycz W (Ed.) (1996) Fuzzy modelling: Paradigms and practice. Kluwer, Dordrecht
88.
go back to reference Pham DT, Karaboga D (1991) Optimum design of fuzzy logic controllers using genetic algorithms. J Syst Eng 1:114–118 Pham DT, Karaboga D (1991) Optimum design of fuzzy logic controllers using genetic algorithms. J Syst Eng 1:114–118
89.
go back to reference Rojas R (1996) Neural networks: a systematic introduction. Springer, Berlin Rojas R (1996) Neural networks: a systematic introduction. Springer, Berlin
90.
go back to reference Sánchez L, Casillas J, Cordón O, del Jesus MJ (2001) Some relationships between fuzzy and random classifiers and models. Int J Approx Reason 29:175–213CrossRef Sánchez L, Casillas J, Cordón O, del Jesus MJ (2001) Some relationships between fuzzy and random classifiers and models. Int J Approx Reason 29:175–213CrossRef
91.
go back to reference Sánchez L, Couso I (2007) Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE Trans Fuzzy Syst 15(4):551–562CrossRef Sánchez L, Couso I (2007) Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE Trans Fuzzy Syst 15(4):551–562CrossRef
92.
go back to reference Sebban M, Nock R, Cahuchat JH, Rakotomalala R (2000) Impact of learning set quality and size on decision tree performance. Int J Comput Syst Signals 1:85–105 Sebban M, Nock R, Cahuchat JH, Rakotomalala R (2000) Impact of learning set quality and size on decision tree performance. Int J Comput Syst Signals 1:85–105
93.
go back to reference Setnes M, Roubos H (2000) GA-fuzzy modeling and classification: complexity and performance. IEEE Trans Fuzzy Syst 8(5):509–522CrossRef Setnes M, Roubos H (2000) GA-fuzzy modeling and classification: complexity and performance. IEEE Trans Fuzzy Syst 8(5):509–522CrossRef
94.
go back to reference Setzkorn C, Paton RC (2005) On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems. BioSystems 81:101–112CrossRef Setzkorn C, Paton RC (2005) On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems. BioSystems 81:101–112CrossRef
95.
go back to reference Shi YH, Eberhart R, Chen YB (1999) Implementation of evolutionary fuzzy systems. IEEE Trans Fuzzy Syst 7(2):109–119CrossRef Shi YH, Eberhart R, Chen YB (1999) Implementation of evolutionary fuzzy systems. IEEE Trans Fuzzy Syst 7(2):109–119CrossRef
96.
go back to reference Smith S (1980) A learning system based on genetic algorithms. PhD Thesis, Unversity of Pittsburgh, Pittsburgh Smith S (1980) A learning system based on genetic algorithms. PhD Thesis, Unversity of Pittsburgh, Pittsburgh
97.
go back to reference Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Trans Syst Man Cybern 15(1):116–132MATH Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Trans Syst Man Cybern 15(1):116–132MATH
98.
go back to reference Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson, Boston Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson, Boston
99.
go back to reference Thrift P (1991) Fuzzy logic synthesis with genetic algorithms. In: Proceedings of 4th international conference on genetic algorithms (ICGA’91), pp 509–513 Thrift P (1991) Fuzzy logic synthesis with genetic algorithms. In: Proceedings of 4th international conference on genetic algorithms (ICGA’91), pp 509–513
100.
go back to reference Tsang C-H, Tsai JH, Wang H (2007) Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognit 40(9):2373–2391MATHCrossRef Tsang C-H, Tsai JH, Wang H (2007) Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognit 40(9):2373–2391MATHCrossRef
101.
go back to reference Valenzuela-Rendon M (1991) The fuzzy classifier system: a classifier system for continuously varying variables. In: Proceedings of 4th international conference on genetic algorithms (ICGA’91), pp 346–353 Valenzuela-Rendon M (1991) The fuzzy classifier system: a classifier system for continuously varying variables. In: Proceedings of 4th international conference on genetic algorithms (ICGA’91), pp 346–353
102.
go back to reference Valenzuela-Rendon M (1998) Reinforcement learning in the fuzzy classifier system. Expert Syst Appl 14:237–247CrossRef Valenzuela-Rendon M (1998) Reinforcement learning in the fuzzy classifier system. Expert Syst Appl 14:237–247CrossRef
103.
go back to reference Wang H, Kwong S, Jin Y, Wei W, Man KF (2005) Multiobjective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets Syst 149:149–186MATHCrossRefMathSciNet Wang H, Kwong S, Jin Y, Wei W, Man KF (2005) Multiobjective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets Syst 149:149–186MATHCrossRefMathSciNet
104.
go back to reference Venturini G (1993) SIA: a supervised inductive algorithm with genetic search for learning attribute based concepts. In: Proceedings of European conference on machine learning. Viena, pp 280–296 Venturini G (1993) SIA: a supervised inductive algorithm with genetic search for learning attribute based concepts. In: Proceedings of European conference on machine learning. Viena, pp 280–296
105.
go back to reference Wilson S (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175 Wilson S (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175
106.
go back to reference Wong ML, Leung KS (2000) Data mining using grammar based genetic programming and applications. Kluwer, Dordrecht Wong ML, Leung KS (2000) Data mining using grammar based genetic programming and applications. Kluwer, Dordrecht
107.
go back to reference Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In: Proceedings of the first European symposium on principles of data mining and knowledge discovery (PKDD), Berlin, pp 78–87 Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In: Proceedings of the first European symposium on principles of data mining and knowledge discovery (PKDD), Berlin, pp 78–87
108.
go back to reference Yager RR, Filev DP (1994) Essentials of fuzzy modeling and control. Wiley, New York Yager RR, Filev DP (1994) Essentials of fuzzy modeling and control. Wiley, New York
109.
go back to reference Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Mak 5(4):597–604CrossRef Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Mak 5(4):597–604CrossRef
110.
go back to reference Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of evolutionary methods for design, optimization and control with applications to industrial problems (EUROGEN2001). Barcelona, pp 95–100 Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of evolutionary methods for design, optimization and control with applications to industrial problems (EUROGEN2001). Barcelona, pp 95–100
Metadata
Title
Genetic fuzzy systems: taxonomy, current research trends and prospects
Author
Francisco Herrera
Publication date
01-03-2008
Publisher
Springer-Verlag
Published in
Evolutionary Intelligence / Issue 1/2008
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-007-0001-5

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