Skip to main content

Advertisement

SpringerLink
Log in

Genetic fuzzy systems: taxonomy, current research trends and prospects

  • Review Article
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6:443–462

    Article  Google Scholar 

  2. 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

    Google Scholar 

  3. 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):521–537

    Article  Google Scholar 

  4. 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 44:45–64

    Article  MATH  Google Scholar 

  5. 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. 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):1035–1064

    Article  MATH  Google Scholar 

  7. 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. Au W-H, Chan KCC, Wong AKC (2006) A fuzzy approach to partitioning continuous attributes for classification. IEEE Trans Knowl Data Eng 18(5):715–719

    Article  Google Scholar 

  9. 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. Bernadó-Mansilla E, Garrell-Guiu JM (2003) Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. Evol Comput 11(3):209–238

    Article  Google Scholar 

  11. 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. 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. Cano JR, Herrera F, Lozano M (2007) Evolutionary stratified training set selection for extracting classification rules with trade-off precision-interpretability. Data Knowl Eng 60:90–108

    Article  Google Scholar 

  14. Carse B, Fogarty TC, Munro A (1996) Evolving fuzzy rule based controllers using genetic algorithms. Fuzzy Sets Syst 80(3):273–293

    Article  Google Scholar 

  15. Casillas J, Carse B, Bull L (2007) Fuzzy-XCS: A Michigan genetic fuzzy system. IEEE Trans Fuzzy Syst 15(4):536–550

    Article  Google Scholar 

  16. 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):135–157

    Article  MATH  Google Scholar 

  17. 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):13–29

    Article  Google Scholar 

  18. Casillas J, Cordón O, Herrera F, Magdalena L (Eds) (2003a) Accuracy improvements in linguistic fuzzy modelling. Springer, Berlin

  19. Casillas J, Cordón O, Herrera F, Magdalena L (Eds) (2003b) Interpretability issues in fuzzy modelling. Springer, Berlin

  20. 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. 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):1013–1031

    Article  Google Scholar 

  22. Cherkassky V, Mulier F (1998) Learning from data: concepts, theory and methods. Wiley, New York

    MATH  Google Scholar 

  23. Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, Kluwer

  24. 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 14:123–1153

    Article  Google Scholar 

  25. 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 141:5–31

    Article  MATH  Google Scholar 

  26. 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):369–407

    Article  MATH  Google Scholar 

  27. Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, Singapore

    MATH  Google Scholar 

  28. 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):187–215

    Article  MATH  Google Scholar 

  29. 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 136:85–107

    Article  MATH  Google Scholar 

  30. 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):667–674

    Article  Google Scholar 

  31. Crockett KA, Bandar Z, Fowdar J, O’Shea J (2006) Genetic tuning of fuzzy inference within fuzzy classifier systems. Expert Syst Appl 23:63–82

    Article  Google Scholar 

  32. 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. Deb K (2001) Multi-objective optimization using evolutionary algorithms.Wiley, New York

  34. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  35. De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13:161–188

    Article  Google Scholar 

  36. 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):578–592

    Article  Google Scholar 

  37. Demsar J (2006) Statistical comparison of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  Google Scholar 

  38. Diettereich T (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10:1895–1924

    Article  Google Scholar 

  39. Dubois D, Prade H, Sudkamp T (2005) On the representation, measurement, and discovery of fuzzy associations. IEEE Trans Fuzzy Syst 13:250–262

    Article  Google Scholar 

  40. Eiben AE, Smith JE (2003) Introduction to evolutionary computation. Springer, Berlin

    Google Scholar 

  41. 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. Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, Berlin

    MATH  Google Scholar 

  43. Geyer-Schulz A (1995) Fuzzy rule-based expert systems and genetic machine learning. Physica-Verlag, Berlin

    Google Scholar 

  44. Giordana A, Neri F (1995) Search-intensive concept induction. Evol Comput 3:375–416

    Google Scholar 

  45. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  46. González A, Pérez R (1999) SLAVE: A genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 27:176–191

    Article  Google Scholar 

  47. González A, Pérez R (2001) Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Trans Syst Man Cybern B Cybern 31(3):417–425

    Article  Google Scholar 

  48. 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. Greene DP, Smith SF (1993) Competition-based induction of decision models from examples. Mach Learn 3:229–257

    Article  Google Scholar 

  50. Gudwin RR, Gomide FAC, Pedrycz W (1998) Context adaptation in fuzzy processing and genetic algorithms. Int J Intell Syst 13(10–11):929–948

    Article  Google Scholar 

  51. Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Mining Knowl Discov 15(1):55–86

    Article  MathSciNet  Google Scholar 

  52. Herrera F (2005) Genetic fuzzy systems: Status, critical considerations and future directions. Int J Comput Intell Res 1(1):59–67

    Google Scholar 

  53. Herrera F, Lozano M, Verdegay JL (1995) Tuning fuzzy-logic controllers by genetic algorithms. Int J Approx Reason 12(3–4):299–315

    Article  MATH  MathSciNet  Google Scholar 

  54. Herrera F, Lozano M, Verdegay JL (1998) A learning process for fuzzy control rules using genetic algorithms. Fuzzy Sets Syst 100:143–151

    Article  Google Scholar 

  55. 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):129–139

    Article  Google Scholar 

  56. Hoffmann F, Schauten D, Hölemann S (2007) Incremental evolutionary design of TSK fuzzy controllers. IEEE Trans Fuzzy Syst 15(4):563–577

    Article  Google Scholar 

  57. Holland JH (1975) Adaptatioon in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  58. 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. 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):1091–1101

    Article  Google Scholar 

  60. Hüllermeier E (2005) Fuzzy methods in machine learning and data mining: Status and prospects. Fuzzy Sets Syst 156(3):387–406

    Article  Google Scholar 

  61. 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. 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):135–150

    Article  Google Scholar 

  63. 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):601–618

    Article  Google Scholar 

  64. Ishibuchi H, Nakashima T, Nii M (2004) Classification and modeling with linguistic information granules: advanced approaches to linguistic data mining. Springer, Berlin

    Google Scholar 

  65. 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): 260–270

    Article  Google Scholar 

  66. 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):59–88

    Article  MATH  MathSciNet  Google Scholar 

  67. 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):290–302

    Article  MathSciNet  Google Scholar 

  68. Karr C (1991) Genetic algorithms for fuzzy controllers. AI Expert 6(2):26–33

    Google Scholar 

  69. Kaya M (2006) Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules. Soft Comput 10(7): 578–586

    Article  MATH  MathSciNet  Google Scholar 

  70. Kaya M, Alhajj R (2005) Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets Syst 152(3): 587–601

    Article  MATH  MathSciNet  Google Scholar 

  71. 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):207–225

    Article  MATH  MathSciNet  Google Scholar 

  72. 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. Konar A (2005) Computational Intelligence: Principles, techniques and applications. Springer, Berlin

    MATH  Google Scholar 

  74. Kovacs T (2004) Strength or accuracy: credit assignment in learning classifier systems. Springer, Berlin

    MATH  Google Scholar 

  75. Kuncheva L (2000) Fuzzy classifier design. Springer, Berlin

    MATH  Google Scholar 

  76. Kweku-Muata, Osey-Bryson (2004) Evaluation of decision trees: a multicriteria approach. Comput Oper Res 31:1933–1945

    Google Scholar 

  77. Lavrač N, Cestnik B, Gamberger D, Flach P (2004) Decision support through subgroup discovery: three case studies and the lessons learned. Mach Learn 57:115–143

    Article  MATH  Google Scholar 

  78. Magdalena L (1997) Adapting the gain of an FLC with genetic algorithms. Int J Approx Reason 17(4):327–349

    Article  MATH  Google Scholar 

  79. Mamdani EH (1974) Applications of fuzzy algorithm for control a simple dynamic plant. Proc IEEE 121(12):1585–1588

    Google Scholar 

  80. 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):1168–1178

    Article  Google Scholar 

  81. Mikut R, Jäkel J, Gröll L (2005) Interpretability issues in data-based learning of fuzzy systems. Fuzzy Sets Syst 150:179–197

    Article  MATH  Google Scholar 

  82. Moriarty DE, Miikkulainen R (1996) Efficient reinforcement learning through symbiotic evolution. Mach Learn 22:11–32

    Google Scholar 

  83. 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. 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. Palm R, Driankov D, Hellendoorn (1997) Model based fuzzy control. Springer, Berlin

    MATH  Google Scholar 

  86. 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):39–47

    Article  Google Scholar 

  87. Pedrycz W (Ed.) (1996) Fuzzy modelling: Paradigms and practice. Kluwer, Dordrecht

  88. Pham DT, Karaboga D (1991) Optimum design of fuzzy logic controllers using genetic algorithms. J Syst Eng 1:114–118

    Google Scholar 

  89. Rojas R (1996) Neural networks: a systematic introduction. Springer, Berlin

    Google Scholar 

  90. Sánchez L, Casillas J, Cordón O, del Jesus MJ (2001) Some relationships between fuzzy and random classifiers and models. Int J Approx Reason 29:175–213

    Article  Google Scholar 

  91. Sánchez L, Couso I (2007) Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE Trans Fuzzy Syst 15(4):551–562

    Article  Google Scholar 

  92. 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

    Google Scholar 

  93. Setnes M, Roubos H (2000) GA-fuzzy modeling and classification: complexity and performance. IEEE Trans Fuzzy Syst 8(5):509–522

    Article  Google Scholar 

  94. Setzkorn C, Paton RC (2005) On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems. BioSystems 81:101–112

    Article  Google Scholar 

  95. Shi YH, Eberhart R, Chen YB (1999) Implementation of evolutionary fuzzy systems. IEEE Trans Fuzzy Syst 7(2):109–119

    Article  Google Scholar 

  96. Smith S (1980) A learning system based on genetic algorithms. PhD Thesis, Unversity of Pittsburgh, Pittsburgh

  97. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    MATH  Google Scholar 

  98. Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson, Boston

    Google Scholar 

  99. Thrift P (1991) Fuzzy logic synthesis with genetic algorithms. In: Proceedings of 4th international conference on genetic algorithms (ICGA’91), pp 509–513

  100. 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):2373–2391

    Article  MATH  Google Scholar 

  101. 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. Valenzuela-Rendon M (1998) Reinforcement learning in the fuzzy classifier system. Expert Syst Appl 14:237–247

    Article  Google Scholar 

  103. 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 149:149–186

    Article  MATH  MathSciNet  Google Scholar 

  104. 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. Wilson S (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175

    Google Scholar 

  106. Wong ML, Leung KS (2000) Data mining using grammar based genetic programming and applications. Kluwer, Dordrecht

  107. 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. Yager RR, Filev DP (1994) Essentials of fuzzy modeling and control. Wiley, New York

  109. Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Mak 5(4):597–604

    Article  Google Scholar 

  110. 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

Download references

Acknowledgments

I would like to first thank to my colleagues Rafael Alcalá and Jesus Alcalá-Fdez who provided much helpful material for Sects. 4.4 and 3.1, respectively, and Alberto Fernández who mantains the paper’s web site. My sincere gratitude is also extended to Brian Carse, Jorge Casillas, Oscar Cordón, Pedro González, Hisao Ishibuchi, Francesco Marcelloni and Luciano Sánchez, for proofreading the manuscript and making helpful suggestions and corrections. This work was supported by the Spanish Ministry of Education and Science (MEC) under Project TIN2005-08386-C05-01.

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Francisco Herrera

Authors
  1. Francisco Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisco Herrera.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Herrera, F. Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol. Intel. 1, 27–46 (2008). https://doi.org/10.1007/s12065-007-0001-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-007-0001-5

Keywords

Search

Navigation