Skip to main content
SpringerLink
Log in

An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic

  • Chapter
Fuzzy Evolutionary Computation

Abstract

This bibliography contains references to contributions published in journals, conference proceeding, books, and theses that deal with genetic algorithms and fuzzy techniques. The references have been collected from the author’s general genetic algorithm bibliography, which contains nearly 7000 references when this special fuzzy collection was compiled.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Proc. of the Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms, Nagoya (Japan), 9.–10. Aug. 1995. Springer-Verlag, Berlin (Germany).

    Google Scholar 

  2. Proc. of the Second IEEE Conf. on Evolutionary Computation (ICEC’95), Perth (Australia), Nov. 1995. IEEE, New York, NY.

    Google Scholar 

  3. In I. Parmee and M. J. Denham, eds., Adaptive Computing in Engineering Design and Control ‘96 (ACEDC’96), 2nd Int. Conf. of the Integration of Genetic Algorithms and Neural Network Computing and Related Adaptive Techniques with Current Engineering Practice, Plymouth (UK), 26.–28. Mar. 1996.

    Google Scholar 

  4. M.-R. Akbarzadeh, K. K. Kumbla, and M. Jamshidi. Genetic algorithms in learning fuzzy hierarchical control of distributed parameter systems. In IEEE-SMC’95 [121], p. 4027–4032.

    Google Scholar 

  5. J. T. Alander. An indexed bibliography of genetic algorithms: Years 1957–1993. Art of CAD Ltd., Vaasa (Finland), 1994. (over 3000 GA references).

    Google Scholar 

  6. J. T. Alander. Genetic algorithms in industrial applications-A bibliography. In J. T. Alander, ed., Proc. of the First Nordic Workshop on Genetic Algorithms and their Applications (1NWGA), Proc. of the University of Vaasa, Nro. 2, p. 301–327, 331–414, Vaasa (Finland), 9.–12. Jan. 1995. (ftp: ftp.uwasa.fi: cs/1NWGA/Bibliography.ps.Z).

    Google Scholar 

  7. J. T. Alander. Indexed bibliography of genetic algorithms with fuzzy systems. Report 94-1-FUZZY, University of Vaasa, Department of Information Technology and Production Economics, 1995. (ftp: ftp.uwasa.fi: cs/report94-l/gaFUZZYbib.ps.Z).

    Google Scholar 

  8. J. T. Alander. In L. Chambers, ed., Practical Handbook of Genetic Algorithms: New Frontiers, Volume 2, chapter Appendix 1. An indexed bibliography of genetic algorithms (Books, proceedings, journal articles, and PhD thesis), p. 333–427. CRC Press, Inc., Boca Raton, FL, 1995.

    Google Scholar 

  9. J. T. Alander, editor. Proc. of the Second Nordic Workshop on Genetic Algorithms and their Applications (2NWGA), Proc. of the University of Vaasa, Nro. 11, Vaasa (Finland), 19.–23. Aug. 1996. (ftp: ftp.uwasa.fi: cs/2NWGA/*.ps.Z).

    Google Scholar 

  10. E. Alba, C. Cotta, and J. J. Troyo. Type-constrained genetic programming for rule-base definition in fuzzy logic controllers. In J. R. Koza et al, eds., Proc. of the GP-96 Conf., Stanford, CA, 28.–31. July 1996. MIT Press, Cambridge, MA.

    Google Scholar 

  11. J. Albert, F. Ferri, J. Domingo, and M. Vincens. An approach to natural scene segmentation by means of genetic algorithms with fuzzy data. In N. Perezdelablanca, A. Sanfeliu, and E. Vidal, eds., 4th National Symposium in Pattern Recognition and Image Analysis, vol. 1, p. 97–112, Granada (Spain), Sept. 1990. World Scientific Publishers Co. Inc.

    Google Scholar 

  12. C. A. Ankenbrandt, B. P. Buckles, and F. E. Petry. Scene recognition using genetic algorithms with semantic nets. Pattern Recognition Letters, 11(4):285–293, 1990.

    MATH  Google Scholar 

  13. C. A. Ankenbrandt, B. P. Buckles, F. E. Petry, and M. Lybanon. Ocean feature recognition using genetic algorithms with fuzzy fitness functions (GA/F3). In E. Griffin, ed., 3rd Annual Workshop on Space Operations Automation and Robotics (SOAR 89), p. 679–686, Lyndon B. Johnson Space Center, Houston, TX, 25.–27. July 1989 1990. NASA, Washington.

    Google Scholar 

  14. M. Arao, Y. Tsutsumi, T. Fukuda, and K. Shimojima. Flexible intelligent system based on fuzzy, neural networks and reinforcement learning. In Proc. of the 1995 IEEE Int. Conf. on Fuzzy Systems, vol. 5, p. 69–70, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  15. S. Arnone, M. Dell’Orto, A. Tettamanzi, and M. Tomassini. Towards a fuzzy government of genetic populations. In Proc. of the 6th IEEE Conf. on Tools with Artificial Intelligence (TAI’94), p. 585–591, New Orleans, LA, 6.–9. Nov. 1994. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  16. F. Ashrafzadeh, E. P. Nowicki, and J. C. Salmon. A self-organizing and self-tuning fuzzy logic controller for field oriented control of induction motor drives. In Proc. of the 1995 IEEE Industry Applications Conf., vol. 2, p. 1656–1662, Orlando, FL, 8.–12. Oct. 1995. IEEE, New York, NY.

    Google Scholar 

  17. C. L. Barczak, C. A. Martin, and C. P. Krambeck. Experiments in fuzzy control using genetic algorithms. In Proc. of the 1994 IEEE Int. Symposium on Industrial Electronics (ISIE’94), p. 426–428, Santiago (Chile), 25.–27. May 1994. IEEE, New York.

    Google Scholar 

  18. F. J. Bartos. Fuzzy logic sharpens its image. Control Eng., 42(8):5pp, 1995.

    Google Scholar 

  19. R. K. Belew and L. B. Booker, editors. Proc. of the Fourth Int. Conf. on Genetic Algorithms, San Diego, 13.–16. July 1991. Morgan Kaufmann Publishers.

    Google Scholar 

  20. A. Bergman, W. Burgard, and A. Hemker. Adjusting parameters of genetic algorithms by fuzzy control rules. In Proc. of the Third Int. Workshop on Software Engineering and Expert Systems for High Energy and Nuclear Physics, New Computing Techniques in Physics Research III, p. 235–240, Oberammergau (Germany), 4.–8. Oct. 1994. World Scientific, Singapore.

    Google Scholar 

  21. H. Bersini. Automatic generation of fuzzy control systems by gradient methods and genetic algorithms. In Les Applications Des Ensembles Flous (Applications of Fuzzy Sets), p. 199–209, Nimes (France), 2.–3. Nov. 1992. EC2, Nanterre Cedex (France), (in French).

    Google Scholar 

  22. J. C. Bezdek and R. J. Hathaway. Optimization of fuzzy clustering criteria using genetic algorithms. In ICEC’94 [119], p. 589–594.

    Google Scholar 

  23. D. Bhandari, S. K. Pal, and M. K. Kundu. Image enhancement incorporating fuzzy fitness function in genetic algorithms. In Second IEEE Int. Conf. on Fuzzy Systems, vol. II, p. 1408–1413, San Francisco, Mar. 28.–Apr. 1. 1993. IEEE.

    Google Scholar 

  24. A. Bonarini. ELF: learning incomplete fuzzy rule sets for an autonomous robot. In Proc. of EUFIT ‘93, p. 69–75, Aachen (Germany), 1993. ELITE Foundation.

    Google Scholar 

  25. A. Bonarini. Evolutionary learning of general fuzzy rules with biased evaluation functions: competition and cooperation. In ICEC’94 [119], p. 51–56.

    Google Scholar 

  26. A. Bonarini. Learning behaviors implemented as fuzzy logic controllers for autonomous agents. In WEC2 [281], p. 21–24.

    Google Scholar 

  27. P. P. Bonissone, V. Badami, K. H. Chiang, P. S. Khedkar, K. W. Marcelle, and M. J. Schutten. Industrial applications of fuzzy logic at General Electric. Proc. of the IEEE, 83(3):450–465, Mar. 1995.

    Google Scholar 

  28. H.-H. Both. Mechanic human head robot controlled by a fuzzy inference engine. In Proc. of the IEEE/IAS Int. Conf. on Industrial Automation and Control Conf., p. 71–76, Hyderabad, India, 5.–7. Jan. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  29. M. Botta, A. Giordana, and L. Saitta. Learning fuzzy concept definition. In Proc. of the Second IEEE Int. Conf. on Fuzzy Systems, p. 18–22, San Francisco, CA, Mar. 28.–Apr. 1. 1993. IEEE New York.

    Google Scholar 

  30. R. Boyd and C. Glass. Interpreting ground-penetrating radar images using object-oriented neural, fuzzy, and genetic processing. In H. N. Nasr, ed., Ground Sensing, vol. SPIE-1941, p. 169–181, Orlando, FL, 14. Apr. 1993. The Int. Society for Optical Engineering.

    Google Scholar 

  31. R. Braunstingl, J. Mujika, and J. P. Ulribe. A wall following robot with a fuzzy logic controller optimized by a genetic algorithm. In Proc. of the 1995 IEEE Int. Conf. on Fuzzy Systems, vol. 5, p. 77–82, Yokohama (Japan), 20.–24. Mar. 1995. IEEE.

    Google Scholar 

  32. B. P. Buckles, F. E. Petry, D. Prabhu, R. George, and R. Srikanth. Fuzzy clustering with genetic search. In ICEC’94 [119], p. 46–50.

    Google Scholar 

  33. J. J. Buckley and Y. Hayashi. Fuzzy genetic algorithms for optimization. In IJCNN’93 [123], p. 725–728.

    Google Scholar 

  34. J. J. Buckley and Y. Hayashi. Fuzzy genetic algorithm and applications. Fuzzy Sets and Systems, 61(2):129–136, 24. Jan. 1994.

    MathSciNet  Google Scholar 

  35. J. J. Buckley, P. Krishnamraju, K. Reilly, and Y. Hayashi. Genetic learning algorithms for fuzzy neural nets. In FuzzyIEEE94 [80].

    Google Scholar 

  36. C. Buhusi. Learning by simulating evolution in automatic fuzzy systems synthesis. In FuzzyIEEE94 [80].

    Google Scholar 

  37. J. M. Cadenas and F. Jiminez. A genetic algorithm for the multiobjective solid transportation problem: a fuzzy approach. In Proc. of the 4th Int. Workshop, Current Issues in Fuzzy Technologies, p. 70–75, Murcia (Spain), 1.–3. June 1994. Villa Madruzzo, Trento, Italy.

    Google Scholar 

  38. R. Caponetto, L. Fortuna, and C. Vinci. Design of fuzzy filters by genetic algorithms. In Proc. of the 1994 IEEE Int. Symposium on Circuits & Systems, vol. 5, p. 177–180, London (UK), 30. May–2. June 1994. IEEE, Piscataway, NJ.

    Google Scholar 

  39. R. Caponetto, L. M. Presti, and C. Vinci. Genetic optimization for the design for an n-step fuzzy controller. In IEEE-SMC’95 [121], p. 849–851.

    Google Scholar 

  40. B. Carse. A fuzzy classifier system using the Pittsburgh approach. In H.-P. Schwefel, and R. Manner, editors. Parallel Problem Solving from Nature-PPSN III, vol. 866 of Lecture Notes in Computer Science, Jerusalem (Israel), 9.–14. Oct. 1994. Springer-Verlag, Berlin Davidor et al. [57].

    Google Scholar 

  41. B. Carse and T. C. Fogarty. Evolutionary learning of temporal behaviour using discrete and fuzzy classifer systems. In Proc. of the 1995 IEEE Int. Symposium on Intelligent Control, p. 183–188, Monterey, CA, 27.–29. Aug. 1995. IEEE, New York, NY.

    Google Scholar 

  42. B. Carse and T. C. Fogarty. Evolutionary learning of controllers using temporal fuzzy classifier systems. In M. J. Denham, eds., Adaptive Computing in Engineering Design and Control ‘96 (ACEDC’96), 2nd Int. Conf. of the Integration of Genetic Algorithms and Neural Network Computing and Related Adaptive Techniques with Current Engineering Practice, Plymouth (UK), 26.–28. Mar. 1996 Parmee and Denham [3].

    Google Scholar 

  43. B. Carse, T. C. Fogarty, and A. Munro. Adaptive distributed routing using evolutionary fuzzy control. In Eshelman [63].

    Google Scholar 

  44. C.-H. Chang and Y.-C. Wu. Genetic algorithm based tuning method for symmetric membership functions of fuzzy logic control system. In Proc. of the 1995 Int. IEEE/IAS Conf. on Industrial Automation and Control: Emerging Technologies, p. 421–428, Taipei (Taiwan), 22.–27. May 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  45. L. Chen, D. H. Cooley, and J. Zhang. Genetic algorithms application to fuzzy classification in remote-sensing. In Proc. of the Fifth Workshop on Neural Networks: Academic/Industrial/NASA /Defence, vol. SPIE-2204, p. 79–83, San Francisco, CA, 7.–10. Nov. 1993. The Int. Society for Optical Engineering.

    Google Scholar 

  46. L. Chen, D. H. Cooley, and J. Zhang. Possibility function-based neural networks: Case study of mathematical analysis. In B. Bosacchi and J. C. Bezdek, eds., Application of Fuzzy Logic Technology III, vol. SPIE-2761, p. 62–75, Orlando, FL, 10.–12. Apr. 1996. The Int. Society for Optical Engineering, Bellingham, WA.

    Google Scholar 

  47. M. Chiaberge, G. D. Bene, S. D. Pascoli, B. Lazzerini, and A. Maggiore. Mixing fuzzy, neural and genetic algorithms in an integrated design environment for intelligent controllers. In IEEE-SMC’95 [121], p. 2988–2993.

    Google Scholar 

  48. D. Chorafas. Chaos theory in the financial markets. Applying fractals. Fuzzy logic. Genetic algorithms. Probus Publishing Co., 1994.

    Google Scholar 

  49. M. G. Cooper. Genetic design of rule-based fuzzy controllers. PhD thesis, University of California, Los Angeles, 1994.

    Google Scholar 

  50. M. G. Cooper. Evolving a rule based fuzzy controller. Simulation, 65(1):67–72, July 1995.

    Google Scholar 

  51. M. G. Cooper and J. J. Vidal. Evolving the size of rule-based fuzzy systems. In Intelligent Systems. Proc. of the Third Golden West Int. Conf., vol. 1, p. 319–326, Las Vegas, NV, 6.–8. June 1994 1995. Kluwer Academic Publishers, Dordrecht.

    Google Scholar 

  52. O. Cordón and F. Herrera. A general study on genetic fuzzy systems. In J. Périaux, M. Galán, and P. Cuesta, editors. Genetic Algorithms in Engineering and Computer Science (EUROGEN95), Las Palmas (Spain), Dec. 1995. John Wiley & Sons, New York Winter et al. [286], p. 33–57.

    Google Scholar 

  53. O. Cordón, F. Herrera, and M. Lozano. On the bidirectional integration of genetic algorithms and fuzzy logic. In WEC2 [281], p. 13–16.

    Google Scholar 

  54. O. Cordón, F. Herrera, and M. Lozano. A three-stage method for designing genetic fuzzy systems by learning from examples. In W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors. Parallel Problem Solving from Nature-PPSN IV, vol. 1141 of Lecture Notes in Computer Science, Berlin (Germany), 22.–26. Sept. 1996. Springer-Verlag, Berlin Voigt et al. [277], p. 720–729.

    Google Scholar 

  55. E. Cox. A model-free trainable fuzzy system for the analysis of financial timeseries data with fuzzy set morphology and rule association optimization through a genetic optimizer. In Proc. of the Second Annual Int. Conf. on Artificial Intelligence Applications on Wall Street: Tactical and Strategic Computing Technologies, p. 280–285, New York, NY, 19.–22. Apr. 1993. Software Engineering Press, Gaithersburg, MD.

    Google Scholar 

  56. J. J. Cupal and B. M. Wilamowski. Selection of fuzzy rules using a genetic algorithm. In Proc. of the World Congress on Neural Networks-San Diego, vol. 1, p. A814–A819, San Diego, CA, 5.–9. June 1994. Lawrence Erlbaum, Hillsdale, NJ.

    Google Scholar 

  57. Y. Davidor, H.-P. Schwefel, and R. Manner, editors. Parallel Problem Solving from Nature-PPSN III, vol. 866 of Lecture Notes in Computer Science, Jerusalem (Israel), 9.–14. Oct. 1994. Springer-Verlag, Berlin.

    MATH  Google Scholar 

  58. G. Deboeck. Neural, genetic, and fuzzy approaches to design of trading systems. In Proc. of the Second Annual Int. Conf. on Artificial Intelligence Applications on Wall Street: Tactical and Strategic Computing Technologies, p. 184–193, New York, NY, 19.–22. Apr. 1993. Software Engineering Press, Gaithersburg, MD.

    Google Scholar 

  59. D. del Castillo Sobrino, J. G. Casao, and C. G.-A. Sanchez. Genetic processing of the sensorial information. Sens. Actuators A. Phys. (Switzerland), A37-A38(2):255–259, 1993. (Proc. of EUROSENSORS VI, San Sebastian (Spain), 5.–7. Oct. 1992).

    Google Scholar 

  60. I. Dumitrache and C. Buiu. Hybrid geno-fuzzy controllers. In IEEE-SMC’95 [121], p. 2034–2039.

    Google Scholar 

  61. A. N. Edmonds, D. Burkhardt, and O. Adjei. Genetic programming of fuzzy logic production rules. In ICEC’95 [2], p. 765–770.

    Google Scholar 

  62. Y. Egusa, H. Akahori, A. Morimura, and N. Wakami. Application of fuzzy set theory for an electronic video camera image stabilizer. IEEE Trans. on Fuzzy Systems, 3(3):351–356, 1995.

    Google Scholar 

  63. L. Eshelman, editor. Proc. of the Sixth Int. Conf. on Genetic Algorithms, Pittsburgh, PA, 15.–19. July 1995.

    Google Scholar 

  64. K. S. et al. A learning algorithm of fuzzy rules using genetic algorithm for MRACS with time-delay. In Proc. of the IECON, Bologna (Italy), Sept. 1994. IEEE, New York.

    Google Scholar 

  65. Proc. of the Second European Congress on Intelligent Techniques and Soft Computing (EUFIT’94), Aachen (Germany), 20.–23. Sept. 1994. ELITE-Foundation.

    Google Scholar 

  66. F. Fagarasan and M. G. Negoita. A genetic-based method for learning the parameters of a fuzzy inference system. In Proc. of the 1995 Second New Zealand Int. Two-Stream Conf. on Artificial Neural Networks and Expert Systems, p. 223–226, Dunedin (New Zealand), 20.–23. Nov. 1995. IEEE Computer Society Press, Los Alamitos.

    Google Scholar 

  67. M. Fathi. Fuzzy-set optimization in use of medical MR-image analysis based on evolution strategies. In AFLNNGA’94 [1].

    Google Scholar 

  68. M. Fathi-Torbaghan and L. Hildebrand. The application of evolution strategies to the problem of parameter optimization in fuzzy rulebased systems. In ICEC’95 [2], p. 825–830.

    Google Scholar 

  69. D. S. Feldman. Fuzzy network synthesis with genetic algorithms. In Forrest [74], p. 312–317.

    Google Scholar 

  70. X. Feng and L. Meyer. A fuzzy stop criterion for genetic algorithms using performance estimation. In FuzzyIEEE94 [80].

    Google Scholar 

  71. G. D. Finn. Learning fuzzy rules by genetic algorithm for a cart-pole balancing system. In Proc. of the ACNN94, p. 121–124, Brisbane, Australia, 1994.

    Google Scholar 

  72. T. C. Fogarty, L. Bull, and B. Carse. Evolving multi-agent systems. In J. Périaux, M. Galán, and P. Cuesta, editors. Genetic Algorithms in Engineering and Computer Science (EUROGEN95), Las Palmas (Spain), Dec. 1995. John Wiley & Sons, New York Winter et al. [286], p. 1–22.

    Google Scholar 

  73. D. B. Fogel and P. K. Simpson. Evolving fuzzy clusters. In 1993 IEEE Int. Conf. on Neural Networks, vol. III, p. 1829–1834, San Francisco, CA, 28. Mar.–1. Apr. 1993. IEEE.

    Google Scholar 

  74. S. Forrest, editor. Proc. of the Fifth Int. Conf. on Genetic Algorithms, Urbana-Champaign, IL, 17.–21. July 1993. Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  75. B. Freisleben and S. Strelen. A hybrid genetic algorithm/fuzzy logic approach to manufacturing process control. In ICEC’95 [2], p. 837–841.

    Google Scholar 

  76. T. Fukuda, Y. Hasegawa, and K. Shimojima. Hierarchical fuzzy reasoning. In ICEC’94 [119], p. 601–606.

    Google Scholar 

  77. T. Fukuda, Y. Hasegawa, K. Shimojima, and F. Saito. Reinforcement learning method for generating fuzzy controller. In ICEC’95 [2], vol. 1, p. 273–278, 1995.

    Google Scholar 

  78. T. Furuhashi. A new approach to genetic based machine learning and an efficient finding of fuzzy rules. In AFLNNGA’94 [1].

    Google Scholar 

  79. T. Furuhashi, K. Nakaoka, and Y. Uchikawa. An efficient finding of fuzzy rules using a new approach to genetic based machine learning. In Proc. of 1995 Int. Conf. on Fuzzy Systems, vol. 2, p. 715–722, Yokohama (Japan), 20.–24. Mar. 1995. IEEE.

    Google Scholar 

  80. Proc. of ICCI94/Fuzzy Systems, Orlando, FL, 26. June–2. July 1994. IEEE.

    Google Scholar 

  81. L. Gacôgne. About the fitness of simulations whose fuzzy rules are learned by genetic algorithms. In EUFIT’94 [65], p. 1523–1531.

    Google Scholar 

  82. L. Gacôgne. Tuning of a fuzzy default system GA. In Evolution Artificielle 95 (EA ‘95), Brest (France), 4.–6. Sept. 1995.

    Google Scholar 

  83. M. Gen, K. Ida, and C. Cheng. Multirow machine layout problem in fuzzy environment using genetic algorithms. In Proc. of the 17th Int. Conf. on Computers and Industrial Engineering, vol. 29, p. 519–523, Phoenix, AZ, 5.–8. Mar. 1995. Comput. Ind. Eng. (UK).

    Google Scholar 

  84. M. Gen, K. Ida, L. Yinzhen, and E. Kubota. Solving bicriteria solid transportation problem with fuzzy numbers by a genetic algorithm. In Proc. of the 17th Int. Conf. on Computers and Industrial Engineering, vol. 29, p. 537–541, Phoenix, AZ, 5.–8. Mar. 1995. Comput. Ind. Eng. (UK).

    Google Scholar 

  85. M. Gen, Y. Tsujimura, and E. Kubota. Solving job-shop scheduling problem with fuzzy processing time using genetic algorithm. In EUFIT’94 [65], p. 1540–1547.

    Google Scholar 

  86. C. Genshe and C. Xinhai. Improved fuzzy logic controller using genetic algorithm and its application to spacecraft rendezvous. In Proc. of the 1993 IEEE Region 10 Conf. on Computer, Communication, Control and Power Engineering (TENCON’93), vol. 4, p. 300–303, Beijing (China), 19.–21. Oct. 1993. IEEE.

    Google Scholar 

  87. R. George and R. Srikanth. Fuzzy logic approach to the summarization of database information. In IEEE-SMC’95 [121], p. 2824–2827.

    Google Scholar 

  88. S. M. George, A. Saxena, and P. RamBabu. Genetic algorithm in the aid of fuzzy rule deduction. In EUFIT’94 [65], p. 1130–1133.

    Google Scholar 

  89. P. Y. Glorennec. Application of genetic algorithms for the optimization of the learning functions of a fuzzy neural net. In Les Applications Des Ensembles Flous (Applications of Fuzzy Sets), p. 219–226, Nimes (Prance), 2.–3. Nov. 1992. EC2, Nanterre Cedex (Prance), (in French).

    Google Scholar 

  90. P. Y. Glorennec. Fuzzy Q-learning and evolutionary strategy for adaptive fuzzy control. In EUFIT’94 [65], p. 35–40.

    Google Scholar 

  91. S. Goonatilake, J. A. Campbell, and N. Ahmad. Genetic-fuzzy hybrid system for financial decision making. In AFLNNGA’94 [1], p. 202–223.

    Google Scholar 

  92. V. Gopalan, A. Homaifar, M. R. Salami, B. Sayyarrodsari, and R. W. Dabney. Fuzzy genetic controllers for the autonomous rendezvous and docking problem. In Proc. of the 1995 ACM Symposium on Applied Computing, p. 532–536, Nashville, TN, 26.–28. Feb. 1995. ACM, New York, NY.

    Google Scholar 

  93. S. K. Halgamuge, H. Genther, and M. Glesner. Fuzzy rule based data analysis using methods of computational intelligence. In Proc. of ISUMA-NAFIPS 95 The Third Int. Symposium on Uncertainty Modeling and Analysis and Annual Conf. of the North American Fuzzy Information Processing Society, p. 76–81, College Park, MD, 17.–20. Sept. 1995. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  94. S. K. Halgamuge and M. Glesner. In L. Chambers, ed., Practical Handbook of Genetic Algorithms: New Frontiers, Volume 2, chapter 14. Input space segmentation with a genetic algorithm for generation of rule based classifier systems, p. 317–331. CRC Press, Inc., Boca Raton, FL, 1995.

    Google Scholar 

  95. L. O. Hall. Genetic fuzzy clustering. In Proc. of the First Int. Joint Conf. of the North American Fuzzy Information Processing Society Biannual Conf.. The Industrial Fuzzy Control and Intelligent Systems Conf., and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic (NAFIPS/IFIS/NASA ‘95), p. 411–415, San Antonio, TX, 18.–21. Dec. 1994. IEEE, New York.

    Google Scholar 

  96. L. O. Hall. A genetic approach to fuzzy clustering. In Proc. of Neural, Parallel and Scientific Computations, vol. 1, page 192, Atlanta, GA, 28.–31. May 1995. Dynamic Publishers, Atlanta, GA.

    Google Scholar 

  97. L. O. Hall and B. Ozyurt. Scaling genetically guided fuzzy clustering. In Proc. of ISUMA-NAFIPS 95 The Third Int. Symposium on Uncertainty Modeling and Analysis and Annual Conf. of the North American Fuzzy Information Processing Society, p. 328–332, College Park, MD, 17.–20. Sept. 1995. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  98. J. Han and W. Ham. A GA-fuzzy controller with sliding mode. In Korea-Australia EC’95 [153], p. 199–205.

    Google Scholar 

  99. U. Hanebeck and G. Schmidt. Optimization of fuzzy networks via genetic algorithms. In EUFIT’94 [65], p. 1011–1013.

    Google Scholar 

  100. T. Hashiyama, T. Furuhashi, and Y. Uchikawa. A study on finding fuzzy rules for semi-active suspension controllers with genetic algorithm. In ICEC’95 [2], vol. 1, p. 279–282, 1995.

    Google Scholar 

  101. F. Herrera, E. Herrera-Viedma, M. Lozano, and J. L. Verdegay. Fuzzy tools to improve genetic algorithms. In EUFIT’94 [65], p. 1532–1539.

    Google Scholar 

  102. F. Herrera and M. Lozano. Heuristic crossovers for real-coded genetic algorithms based on fuzzy connectives. In W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors. Parallel Problem Solving from Nature-PPSN IV, vol. 1141 of Lecture Notes in Computer Science, Berlin (Germany), 22.–26. Sept. 1996. Springer-Verlag, Berlin Voigt et al. [277], p. 336–345.

    Google Scholar 

  103. F. Herrera, M. Lozano, and J. L. Verdegay. Applying genetic algorithms in fuzzy optimization problems. Fuzzy Sets & Artificial Intelligence, 3:39–52, 1994.

    Google Scholar 

  104. F. Herrera, M. Lozano, and J. L. Verdegay. Tuning fuzzy-logic controllers by genetic algorithms, 1995. (paper presented at CIFT’93 workshop on Current Issues on Fuzzy Logic, Rongecno (Trento) Italy, 3.–4. June 1993).

    Google Scholar 

  105. F. Herrera, M. Lozano, and J. L. Verdegay. The use of fuzzy connectives to design real-coded genetic algorithms. Mathware & Soft Computing, 1(3):239–251, 1995.

    MathSciNet  Google Scholar 

  106. T. Hessburg, M. A. Lee, H. Takagi, and M. Tomizuka. Automatic design of fuzzy-systems using genetic algorithms and its application to lateral vehicle guidance. In B. Bosacchi and J. C. Bezdek, eds., Applications of Fuzzy Logic Technology, vol. SPIE-2061, p. 452–463, Boston, MA, 8.–10. Sept. 1993. The Int. Society for Optical Engineering.

    Google Scholar 

  107. K. Hirota. Fuzzy-neuro-chaos: research and industrial applications in Japan. In IEEE-SMC’95 [121], p. 2446–2459.

    Google Scholar 

  108. F. Hoffmann and G. Pfister. Automatic design of hierarchical fuzzy controllers using genetic algorithms. In EUFIT’94 [65], p. 1516–1522.

    Google Scholar 

  109. M. Höhfeld. Industrial applications of evolutionary algorithms at Siemens AG. In Alander [9], p. 183–194. (ftp: ftp.uwasa.fi: cs/2NWGA/Hoehfeld.ps.Z).

    Google Scholar 

  110. A. Homaifar and V. E. McCormick. Full design of fuzzy controllers using genetic algorithms. In S.-S. Chen, ed., Neural and Stochastic Methods in Image and Signal Processing, vol. SPIE-1766, p. 393–404, San Diego, CA, 20.–23. July 1992. The Int. Society for Optical Engineering.

    Google Scholar 

  111. A. Homaifar and V. E. McCormick. Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans. on Fuzzy Systems, 3(2):129–139, May 1995.

    Google Scholar 

  112. C.-C. Hsu, S. ichi Yamada, H. Fujikawa, and K. Shida. MRFACS with nonlinear concequents by fuzzy identification of system for time delay system. In Proc. of the Int. Conf. on Fuzzy Systems, vol. 1, p. 283–288, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  113. C.-C. Hsu, S. ichi Yamada, H. Fujikawa, and K. Shida. A multi-operator self-tuning genetic algorithm for fuzzy control rule optimization. In M. J. Denham, eds., Adaptive Computing in Engineering Design and Control ‘96 (ACEDC’96), 2nd Int. Conf. of the Integration of Genetic Algorithms and Neural Network Computing and Related Adaptive Techniques with Current Engineering Practice, Plymouth (UK), 26.–28. Mar. 1996 Parmee and Denham [3].

    Google Scholar 

  114. Y.-P. Huang and C.-H. Huang. A genetic-based fuzzy grey prediction model. In IEEE-SMC’95 [121], p. 1051–1056.

    Google Scholar 

  115. H.-S. Hwang, Y. H. Joo, H. K. Kim, and K.-B. Woo. Identification of fuzzy control rules utilizing genetic algorithms and its application to mobile robots. In P. J. Fleming and W. H. Kwon, eds., Algorithms and Architectures for Real-Time Control (Korea, 1992), p. 249–254, Seoul (South Korea), Aug. 31.-Sept. 2. 1992. Pergamon Press.

    Google Scholar 

  116. W.-R. Hwang. Intelligent control based on fuzzy algorithms and genetic algorithms. PhD thesis, New Mexico State University, 1993.

    Google Scholar 

  117. W.-R. Hwang and W. E. Thompson. Design of intelligent fuzzy logic controllers using genetic algorithms. In FuzzyIEEE94 [80].

    Google Scholar 

  118. W.-R. Hwang and W. E. Thompson. Genetic algorithms for learning and design of optimal fuzzy trackers. In Signal Processing, Sensor Fusion, and Target Recognition IV, vol. SPIE-2484, p. 154–162, Orlando, FL, 17.–19. Apr. 1995. The Int. Society for Optical Engineering, Bellingham, WA.

    Google Scholar 

  119. Proc. of the First IEEE Conf. on Evolutionary Computation, Orlando, FL, 27.–29. June 1994. IEEE, New York, NY.

    Google Scholar 

  120. T. Ichimura, T. Takano, and E. Tazaki. Reasoning and learning method for fuzzy rules using neural networks with adaptive structured genetic algorithm. In IEEE-SMC’95 vol. 4, p. 3269–3274.

    Google Scholar 

  121. Proc. of the 1995 IEEE Int. Conf. on Systems, Man and Cybernetics, Vancouver, BC (Canada), 22.–25. Oct. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  122. Proc. of the First IEE/IEEE Int. Conf. on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield (UK), 12.–14. Sept. 1995. IEEE.

    Google Scholar 

  123. IJCNN’93-NAGOYA Proc. of 1993 Int. Joint Conf. on Neural Networks, Nagoya (Japan), 25.–29. Oct. 1993. IEEE.

    Google Scholar 

  124. H. Ishibuchi, T. Nakashima, and T. Murata. A fuzzy classifier system that generates fuzzy if-then rules for pattern classification problems. In ICEC’95 [2], p. 759–764.

    Google Scholar 

  125. H. Ishibuchi, K. Nozaki, and N. Yamamoto. Selecting fuzzy rules by genetic algorithm for classification problems. In Second IEEE Int. Conf. on Fuzzy Systems, vol. II, p. 1119–1124, San Francisco, Mar. 28.-Apr. 1. 1993. IEEE.

    Google Scholar 

  126. H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka. Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms. Fuzzy Sets and Systems, 65(2–3):237–253, 10. Aug. 1994.

    MathSciNet  Google Scholar 

  127. H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka. Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans. on Fuzzy Systems, 3(3):260–270, Aug. 1995.

    Google Scholar 

  128. H. Ishigami, T. Fukuda, T. Shibata, and F. Arai. Structure optimization of fuzzy neural network by genetic algorithm. Fuzzy Sets and Systems, 72(3):257–264, 1995.

    Google Scholar 

  129. L. C. Jain. Hybrid connectionist systems in research and teaching. IEEE Aerospace Electronics Syst. Mag., 10(3):14–18, Mar. 1995.

    Google Scholar 

  130. C. Z. Janikow. A genetic algorithm method for optimizing fuzzy decision trees. Information Sciences (USA), 89(3–4):275–296, 1996.

    MathSciNet  Google Scholar 

  131. J.-Y. Jeon and J.-H. Kim. High precision controller design using evolutionary programming. In Korea-Australia EC’95 [153], p. 119–127.

    Google Scholar 

  132. V. Jerabek and G. Lachiver. Micro-genetic algorithms in the optimisation of neuro-fuzzy controllers. In 1995 Canadian Conf. on Electrical and Computer Engineering, vol. 1, p. 109–112, 1995.

    Google Scholar 

  133. Y. H. Joo, H.-S. Hwang, K.-B. Woo, and K. B. Kim. Fuzzy system modeling and its application to mobile robot control. In Z. Bien and K. C. Min, eds., Fuzzy Logic and Its Applications to Engineering, Information Sciences, and Intelligent Systems, Proc. of the 5th IFSA World Congress, p. 147–156, Seoul (South Korea), 1995. Kluwer Academic Publishers, New York.

    Google Scholar 

  134. J. Kacprzyk. Multistage control of a fuzzy system using a genetic algorithm. In ICEC’95 [2], p. 842-.

    Google Scholar 

  135. A. Kandel and M. Schneider. Fuzzy intelligent hybrid systems and their applications. In Proc. of the IEEE Int. Conf. on Fuzzy Systems, vol. 4, p. 2275–2280, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  136. C. L. Karr. Applying genetics to fuzzy logic. AI Expert, 6(3):38–43, Mar. 1991.

    MathSciNet  Google Scholar 

  137. C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26–33, Feb. 1991.

    Google Scholar 

  138. C. L. Karr. Adaptive process control with fuzzy logic and genetic algorithms. Sci. Comput. Autom. (USA), 9(10):23–24, 26, 28–30, 1993.

    Google Scholar 

  139. C. L. Karr. Adaptive control with fuzzy logic and genetic algorithms. In R. R. Yager and L. A. Zadeh, eds., Fuzzy Sets, Neural Networks, and Soft Computing, p. 345–367. Van Nostrand Reinhold, New York, 1994.

    Google Scholar 

  140. C. L. Karr, L. M. Freeman, and D. L. Meredith. Improved fuzzy process-control of spacecraft autonomous rendezvous using a genetic algorithm. In G. Rodriguez, ed., Intelligent Control and Adaptive Systems, vol. SPIE-1196, p. 274–288, Philadelphia, PA, 7.–8. Nov. 1989 1990. SPIE-Int. Society for Optical Engineering.

    Google Scholar 

  141. C. L. Karr and E. J. Gentry. Application of fuzzy control techniques to a chaotic system. In Proc. of the Symposium on Emerging Computer Techniques for the Minerals Industry, p. 371–376, Littleton, CO, Feb. 1993. Society for Mining, Metallurgy et Exploration Inc.

    Google Scholar 

  142. C. L. Karr and E. J. Gentry. Fuzzy control of pH using genetic algorithms. IEEE Trans. on Fuzzy Systems, 1(1):46–52, 1993.

    Google Scholar 

  143. C. L. Karr, D. L. Meredith, and D. A. Stanley. Fuzzy process-control with a genetic algorithm. In R. K. Rajamani and J. A. Herbst, eds., Control ‘90-Mineral and Metallurgical Processing, p. 53–60. Society for Mining, Metallurgy, and Exploration, Inc., Littleton, Colorado, Salt Lake City, 1990.

    Google Scholar 

  144. C. L. Karr and S. K. Sharma. An adaptive process-control system based on fuzzy-logic and genetic algorithms. In Proc. of the 1994 American Control Conf., vol. 3, p. 2470–2474, Baltimore, MD, June 29.-July 1. 1994. IEEE, New York.

    Google Scholar 

  145. C. L. Karr, S. K. Sharma, W. J. Hatcher, and T. R. Harper. Fuzzy control of an exothermic chemical reaction using genetic algorithms. Engineering Applications of Artificial Intelligence, 6(6):575–582, Dec. 1993.

    Google Scholar 

  146. C. L. Karr and D. A. Stanley. Fuzzy linguistic modelling of engineering data using a genetic algorithm. In Proc. of the Artificial Neural Networks in Engineering Conf., p. 339–344, St. Louis, MO, 13.–16. Nov. 1994. ASME, New York.

    Google Scholar 

  147. J. Kim, Y. Moon, and B. P. Zeigler. Designing fuzzy net controllers using genetic algorithms. IEEE Control Systems, 15(3):66–72, June 1995.

    Google Scholar 

  148. J. Kim and B. P. Zeigler. Hierarchical distributed genetic algorithms: A fuzzy logic controller design application. IEEE Expert, 11(3):76–84, June 1996.

    Google Scholar 

  149. K.-C. Kim and J.-H. Kim. Evolutionary programming based multicriteria fuzzy expert system. In Korea-Australia EC’95 [153], p. 58–76.

    Google Scholar 

  150. S. Y. Kim, L. Song, J. C. Son, and S. Kim. Performance characteristics of the fuzzy sign detector. Fuzzy Sets and Systems, 74(2): 195–205, 1995.

    Google Scholar 

  151. Y. H. Kim, H. C. Cho, Y. K. Choi, and H. T. Jeon. On design of the self-organizing fuzzy logic system based on genetic algorithm. In Z. Bien and K. C. Min, eds., Fuzzy Logic and Its Applications to Engineering, Information Sciences, and Intelligent Systems, Proc. of the 5th IFSA World Congress, p. 101–110, Seoul (South Korea), 1995. Kluwer Academic Publishers, New York.

    Google Scholar 

  152. F. Klawonn, J. Kinzel, and R. Kruse. Modifications of genetic algorithms for designing and optimizing fuzzy controllers. In ICEC’94 [119], p. 28–33.

    Google Scholar 

  153. The Korea Science Engineering Foundation, The Australian Academy of Science, The Australian Academy of Technological Sciences and Engineering. Proc. of the 1st Korea-Australia Joint Workshop on Evolutionary Computation, Taejon (Korea), 26.–29. Sept. 1995. KAIST, Korea.

    Google Scholar 

  154. E. Koskimäki and J. Göös. Fuzzy fitness function for electric machine design by genetic algorithm. In Alander [9], p. 237–244. (ftp: ftp.uwasa.fi: cs/2NWGA/Koskimaki.ps.Z).

    Google Scholar 

  155. K. KrishnaKumar and A. Satyadas. Discovering multiple fuzzy models using genetic algorithms and its application. In Proc. of the 10th AIAA Computing in Aerospace Conf., p. 357–365, San Antonio, TX, 28.–30. Mar. 1995. American Institute of Aeronautics and Astronautics, Washington, DC.

    Google Scholar 

  156. K. KrishnaKumar and A. Satyadas. Evolving multiple fuzzy models and its application to an aircraft control problem. In J. Périaux, M. Galán, and P. Cuesta, editors. Genetic Algorithms in Engineering and Computer Science (EUROGEN95), Las Palmas (Spain), Dec. 1995. John Wiley & Sons, New York Winter et al. [286], p. 305–320.

    Google Scholar 

  157. F. Kruggel and P. Bartenstein. Automatical registration of brain vol. data sets. In Y. Bizais, C. Barillot, and R. D. Paola, eds., Proc. of the 14th Int. Conf. on Information Processing in Medical Imaging (IPMI 95), Brest (France), June 1995. Kluwer Academic Publishers, Dordrecht, The Netherlands.

    Google Scholar 

  158. L. I. Kuncheva. Selection of a k-NN reference set by genetic algorithm and index of fuzziness. In EUFIT’94 [65], p. 640–644.

    Google Scholar 

  159. T. V. Le. Evolutionary fuzzy clustering. In ICEC’95 [2], p. 753–758.

    Google Scholar 

  160. T. V. Le. A fuzzy evolutionary approach to solving constraint problems. In ICEC’95 [2], vol. 1, p. 317–319, 1995.

    Google Scholar 

  161. K.-T. Lee, K.-T. Jean, and Y.-Y. Chen. Genetic-based reinforcement learning of fuzzy logic control systems. In IEEE-SMC’95 [121], p. 1057–1060.

    Google Scholar 

  162. M. A. Lee. Automatic design and adaptation of fuzzy systems and genetic algorithms using soft computing techniques. PhD thesis, University of California, Davis, 1994.

    Google Scholar 

  163. M. A. Lee. On genetic representation of high dimensional fuzzy systems. In Proc. of the 3rd Int. Symposium on Uncertainty Modeling and Analysis and Annual Conf. of the North American Fuzzy Information Processing Society, p. 752–757, College Park, MD, 17.–20. Sept. 1995. IEEE.

    Google Scholar 

  164. M. A. Lee, H. Esbensen, and L. Lemaitre. The design of hybrid fuzzy/evolutionary multiobjective optimization algorithms. In Proc. of the 1995 IEEE/Nagoya University World Wisepersons Workshop (WWW95) on Fuzzy Logic and Neural Networks/Evolutionary Computation, p. 118–125, Nagoya, Japan, 14.–15. Nov. 1995. Nagoya Univ. (Nagoya, Japan).

    Google Scholar 

  165. M. A. Lee and M. H. Smith. Automatic design and tuning of a fuzzy system for controlling the Acrobot using genetic algorithms, DSFS, and meta-rule techniques. In Proc. of the First Int. Joint Conf. of the North American Fuzzy Information Processing Society Biannual Conf., p. 416–420, San Antonio, TX, 18.–21. Dec. 1994. IEEE, New York.

    Google Scholar 

  166. M. A. Lee and H. Takagi. Dynamic control of genetic algorithms using fuzzy logic techniques. In Forrest [74], p. 76–83.

    Google Scholar 

  167. M. A. Lee and H. Takagi. Integrating design stages of fuzzy systems using genetic algorithm. In Second IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE’93), vol. I, p. 612–617, San Francisco, Mar. 28.–Apr. 1. 1993. IEEE.

    Google Scholar 

  168. P. G. Lee, K. K. Lee, and G. J. Jeon. Index of applicability for the decomposition method of multivariable fuzzy systems. IEEE Trans. on Fuzzy Systems, 3(3):364–369, 1995.

    Google Scholar 

  169. Y.-G. Lee and Y.-C. Hsueh. Genetic algorithm and fuzzy logic approach for image smoothing. Pattern Recognit. Image Anal. (Russia), 5(4):564–569, 1995.

    Google Scholar 

  170. Y.-G. Lee, J.-H. Lee, and Y.-C. Hsueh. Genetic-based fuzzy hit-or-miss texture spectrum for texture analysis. Electronics Letters, 31(23):1986–1988, 9. Nov. 1995.

    Google Scholar 

  171. M. Lehotsky, V. Olej, and J. Chmumy. Pattern recognition based on the fuzzy neural networks and their learning by modified genetic algorithms. Neural Network World, 5(1):91–97, 1995.

    Google Scholar 

  172. D. D. Leitch. Context dependent coding in genetic algorithms for the design of fuzzy systems. In AFLNNGA’94 [1].

    Google Scholar 

  173. D. D. Leitch. A New Genetic Algorithm for the Evolution of Fuzzy Systems. PhD thesis, Oxford University, Engineering Science Department, 1995. (ftp: ftp.robots.ox.ac.uk: /pub/outgoing/don//thesis.ps.Z).

    Google Scholar 

  174. D. D. Leitch and P. Probert. Genetic algorithms for the development of fuzzy controllers for autonomous guided vehicles. In EUFIT’94 [65], p. 464–469. (ftp: ftp.robots.ox.ac.uk: /pub/outgoing/don//eufit94.ps.Z).

    Google Scholar 

  175. Y. H. Lim and D. H. Park. Optimization of the fuzzy inference model using the hybrid mechanism of genetic algorithms and neural network. J. Korea Inf. Sci. Soc. (South Korea), 22(5):766–775, 1995.

    Google Scholar 

  176. S.-C. Lin and Y.-Y. Chen. GA-based fuzzy controller with sliding mode. In Proc. of the IEEE international Conf. on Fuzzy Systems, vol. 3, p. 1103–1110, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  177. D. A. Linkens and H. O. Nyongesa. A real-time genetic algorithm for fuzzy control. In Proc. of the IEE Colloquium on Genetic Algorithms for Control and Systems Engineering, vol. Digest No. 1992/106, p. 9/1–9/4, London, 8. May 1992. IEE, London.

    Google Scholar 

  178. D. A. Linkens and H. O. Nyongesa. A distributed genetic algorithm for multivariable fuzzy control. In Proc. of the IEE Colloquium on Genetic Algorithms for Control and Systems Engineering, vol. Digest No. 1993/130, p. 9/1–9/3, London, 28. May 1993. IEE, London.

    Google Scholar 

  179. D. A. Linkens and H. O. Nyongesa. Genetic algorithms for fuzzy control. 2. online system development and application. IEE Proc, Control Theory Appl. (UK), 142(3):177–185, 1995.

    MATH  Google Scholar 

  180. J. Liska and S. Melsheimer. Complete design of fuzzy logic systems using genetic algorithms. In FuzzyIEEE94 [80].

    Google Scholar 

  181. J. Liu and W. Xie. A genetics-based approach to fuzzy clustering. In Proc. of the 1995 IEEE Int. Conf. on Fuzzy Systems, vol. 4, p. 2233–2240, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, New York, NY.

    Google Scholar 

  182. A. Loskiewicz-Buczak and R. E. Uhrig. Determination of fuzzy decision fusion system parameters by genetic algorithms. In Applications of artificial neural networks V, vol. SPIE-2243, p. 142–153, Orlando, FL, 5.–8. Apr. 1994. The Int. Society for Optical Engineering.

    Google Scholar 

  183. A. Loskiewicz-Buczak and R. E. Uhrig. Information fusion by fuzzy set operation and genetic algorithms. Simulation, 65(1):51–66, 1995.

    Google Scholar 

  184. R. J. Machado and A. F. da Rocha. Evolutive fuzzy neural networks. In Proc. of the 1992 IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), p. 493–500, San Diego, CA, 8.–12. Mar. 1992. IEEE, New York.

    Google Scholar 

  185. L. Magdalena. Estudio de la coordinación inteligente en robots bípedos: aplicación de lógica borrosa y algoritmos genéticos. PhD thesis, Univ. Politécnica de Madrid, 1994.

    Google Scholar 

  186. L. Magdalena, J. R. Velasco, G. Fernández, and F. Monasterio. Evolutionary-based learning applied to fuzzy controllers. In Proc. of the 4th IEEE Int. Conf. on Fuzzy Systems and the 2nd Int. Fuzzy Engineering Symposium, vol. III, p. 1111–1118, Mar. 1994. IEEE, New York.

    Google Scholar 

  187. M. Makrehchi. Application of genetic algorithms in fuzzy rules generation. In ICEC’95 [2], vol. 1, p. 251–256, 1995.

    Google Scholar 

  188. W. Meng and F. Guangzeng. Multisolution learning algorithm for fuzzy rules. In Proc. of the 1995 IEEE Int. Symposium on Circuits and Systems-ISCAS 95, vol. 1, p. 478–481, Seattle, WA, Apr. 30.–May 3. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  189. G. Mester. Neuro-fuzzy-genetic controller design for robot manipulators. In Proc. of the 21st Int. Conf. on Industrial Electronics, Control and Instrumentation, vol. 1, p. 87–92, Orlando, FL, 6.–10. Nov. 1995. IEEE, New York, NY.

    Google Scholar 

  190. V. Miranda and L. M. Proenca. Genetic algorithms and fuzzy models-an application to gas and electricity distribution planning under uncertainty. In Proc. of the Third Int. Workshop on Rough Sets and Soft Computing, p. 43–50, San Jose, CA, 10.–12. Nov. 1994. San Jose State University, San Jose, CA. (available via www URL: www.inescn.ps/ acsilva/papers.html).

    Google Scholar 

  191. V. Miranda and L. M. Proenca. A general methodology for distribution planning under uncertainty, including genetic algorithms and fuzzy models in a multi-criteria environment. In Proc. of IEEE/KTH Stockholm Power Tech Conf., p. 832–837, Stockholm (Sweden), June 1995. KTH Stockholm (Sweden).

    Google Scholar 

  192. H. Mizunuma and J. Watada. Fuzzy mixed integer programming based on genetic algorithm and its application to resource distribution. Jpn. J. Fuzzy Theory Syst. (USA), 7(1):97–117, 1995.

    MathSciNet  Google Scholar 

  193. M. Mohammadian and R. J. Stonier. Tuning and optimisation of membership functions of fuzzy logic controllers by genetic algorithms. In Proc. of the 3rd IEEE Int. Workshop on Robot and Human Communication, p. 356–361, Nagoya, 18.–20. July 1994. IEEE.

    Google Scholar 

  194. M. Mohammadian and R. J. Stonier. Adaptive two layer fuzzy control of a mobile robot system. In ICEC’95 [2], vol. 1, p. 204–208, 1995.

    Google Scholar 

  195. K. Morikawa, T. Furuhashi, and Y. Uchikawa. Controlling excessive fuzzyness in a fuzzy classifier system. In Forrest [74].

    Google Scholar 

  196. H. Mühlenbein. The science of breeding and its application to genetic algorithms. In J. Périaux, M. Galán, and P. Cuesta, editors. Genetic Algorithms in Engineering and Computer Science (EUROGEN95), Las Palmas (Spain), Dec. 1995. John Wiley & Sons, New York Winter et al. [286], p. 59–82.

    Google Scholar 

  197. T. Murai, H. Kanemitsu, M. Miyakoshi, and M. Shimbo. Interactive query specification in fuzzy document retrieval systems using genetic algorithms. In Proc. of the Applications of Fuzzy Logic Technology, vol. 2493, p. 328–339, Orlando, FL, 19.–21. Apr. 1995. SPIE-The Int. Society for Optical Engineering.

    Google Scholar 

  198. T. Murata and H. Ishibuchi. Adjusting membership functions of fuzzy classification rules by genetic algorithms. In Proc. of the FUZZ-IEEE/IFES’95, p. 1819–1824, Yokohama (Japan), 20.–24. Mar. 1995. IEEE.

    Google Scholar 

  199. J. Muruzabal. Fuzzy and probabilistic reasoning in simple learning classifier systems. In ICEC’95 [2], vol. 1, p. 262–266, 1995.

    Google Scholar 

  200. J. Muruzabal and A. Munoz. Diffuse pattern learning with fuzzy ARTMAP and PASS. In H.-P. Schwefel, and R. Manner, editors. Parallel Problem Solving from Nature-PPSN III, vol. 866 of Lecture Notes in Computer Science, Jerusalem (Israel), 9.–14. Oct. 1994. Springer-Verlag, Berlin Davidor et al. [57].

    Google Scholar 

  201. G. Nakamiti and F. Gomide. An evolutive fuzzy mechanism based on past experiences. In EUFIT’94 [65], p. 1211–1217.

    Google Scholar 

  202. S. Nakanishi, A. Ohtake, R. R. Yager, S. Ohtani, and H. Kikuchi. Structure identification of acquired knowledge in fuzzy inference by genetic algorithms. In Proc. of the 1995 IEEE/Nagoya University World Wisepersons Workshop (WWW95) on Fuzzy Logic and Neural Networks/Evolutionary Computation, p. 42–48, Nagoya, Japan, 14.–15. Nov. 1995. Nagoya Univ. (Nagoya, Japan).

    Google Scholar 

  203. K. Nakaoka, T. Furuhashi, and Y. Uchikawa. A study on apportionment of credits of fuzzy classifier system for knowledge acquisition of large scale systems. In FuzzyIEEE94 [80].

    Google Scholar 

  204. M. G. Negoita. The fusion of genetic algorithms and fuzzy logic: Applications in the expert systems and intelligent control. In AFLNNGA’94 [1].

    Google Scholar 

  205. M. G. Negoita, F. Fagarasan, and A. Agapie. Applications of genetic algorithms in solving fuzzy relational equations. In EUFIT’94 [65], p. 1126–1129.

    Google Scholar 

  206. K. C. Ng and Y. Li. Design of sophisticated fuzzy logic controllers using genetic algorithms. In FuzzyIEEE94 [80].

    Google Scholar 

  207. K. C. Ng, Y. Li, D. J. Murray-Smith, and K. C. Sharman. Genetic algorithms applied to fuzzy sliding mode controller design. In IEE/IEEE Sheffield ‘95 [122], p. 220–225.

    Google Scholar 

  208. H. Nomura, I. Hayashi, and N. Wakami. A self-tuning method of fuzzy reasoning by genetic algorithm. In Proc. of the Int. Fuzzy Systems and Intelligent Control Conf. (IFSICC’92), p. 236–245, Louisville, KY, 1992.

    Google Scholar 

  209. J. Ohwi, S. Ulyanov, and K. Yamafuji. GA in continuous space and fuzzy classifier system for opening of door with manipulator of mobile robot: new benchmark of evolutionary intelligent computing. In ICEC’95 [2], vol. 1, p. 257–261, 1995.

    Google Scholar 

  210. K. Ong and Q.-H. Wang. Generalized fuzzy reasoning algorithm for an object-oriented expert system tool. Expert Systems, 12(3):199–207, 1995.

    Google Scholar 

  211. G. Ortega. Genetic algorithms for fuzzy control of automatic docking with a space station. In ICEC’95 [2], vol. 1, p. 157–161, 1995.

    Google Scholar 

  212. P. Osmera. The optimization of parameters of PID and fuzzy controllers. In P. Osmera, ed., Proc. of the MENDEL’95, p. 109–116, Brno (Czech Republic), 26.–28. Sept. 1995. Technical University of Brno.

    Google Scholar 

  213. P. Ošmera. Optimization of parameters of fuzzy controllers by genetic algorithms. In WEC2 [281], p. 17–20.

    Google Scholar 

  214. N. R. Pal and J. C. Bezdek. On cluster validity for the fuzzy c-means model. IEEE Trans. on Fuzzy Systems, 3(3):370–379, 1995.

    Google Scholar 

  215. S. K. Pal. Fuzzy sets in image processing and recognition. In Proc. of the 1992 IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), p. 119–126, San Diego, CA, 8.–12. Mar. 1992. IEEE, New York.

    Google Scholar 

  216. S. K. Pal and D. Bhandari. Genetic algorithms with fuzzy fitness function for object extraction using cellular networks. Fuzzy Sets and Systems, 65(2–3):129–139, 10. Aug. 1994.

    Google Scholar 

  217. Y.-H. Pao. A computational intelligence approach to intelligent systems: interrelationships between neural net computing, evolutionary programming, fuzzy sets and expert systems. In Proc. of the Int. Conf. on Intelligent System Application to Power Systems, Montpellier (France), 5.–9. Sept. 1994.

    Google Scholar 

  218. D. Park, A. Kandel, and G. Langholz. Genetic-based new fuzzy reasoning models with application to fuzzy control. IEEE Trans. on Systems, Man, and Cybernetics, 24(1):39–47, Jan. 1994.

    Google Scholar 

  219. Y. J. Park, H. S. Cho, and D. H. Cha. Genetic algorithm-based optimization of fuzzy logic controller using characteristic parameters. In ICEC’95 [2], p. 831–836.

    Google Scholar 

  220. A. Parodi and P. Bonelli. A new approach to fuzzy classifier systems. In Forrest [74], p. 223–230.

    Google Scholar 

  221. K. M. Passino. Intelligent control for autonomous systems. IEEE Spectrum, 32(6):55–62, June 1995.

    Google Scholar 

  222. R. Pearce and P. H. Cowley. Use of fuzzy logic to overcome constraint problems in genetic algorithms. In IEE/IEEE Sheffield ‘95 [122], p. 13–17.

    Google Scholar 

  223. W. Pedrycz. Genetic algorithms for learning in fuzzy relational structures. Fuzzy Sets and Systems, 69(1):37–52, Jan. 1995.

    Google Scholar 

  224. C. Perneel and M. Acheroy. Fuzzy reasoning and genetic algorithms for decision making problems in uncertain environment. In Proc. of the First Int. Joint Conf. of the North American Fuzzy Information Processing Society Biannual Conf.. The Industrial Fuzzy Control and Intelligent Systems Conf., and The NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic, p. 115–120, San Antonio, TX, 18.–21. Dec. 1994. IEEE, New York.

    Google Scholar 

  225. C. Perneel, J.-M. Themlin, J.-M. Renders, and M. Acheroy. Optimization of fuzzy expert systems using genetic algorithms and neural networks. IEEE Trans. on Fuzzy Systems, 3(3):300–312, Aug. 1995.

    Google Scholar 

  226. D. T. Pham and G. Jin. Evolutionary design of an adaptive fuzzy logic controller for processes with time delays. In Proc. of the 1994 IEEE Int. Conf. on Systems, Man, and Cybernetics, vol. 1, p. 431–436, San Antonio, TX, 2.–5. Oct. 1994. IEEE.

    Google Scholar 

  227. D. T. Pham and D. Karaboga. Optimum design of fuzzy logic controllers using genetic algorithms. Journal of Systems Engineering, 1(2):114–118, 1991.

    Google Scholar 

  228. D. T. Pham and D. Karaboga. Design of neuromorphic fuzzy controllers. In 1993, Int. Conf. on Systems, Man and Cybernetics, vol. 4, p. 103–108, Le Touquet (France), 17.–20. Oct. 1993. IEEE, New York.

    Google Scholar 

  229. B. Porter and N. N. Zadeh. Genetic design of fuzzy-logic controllers for robotic manipulators. In ICEC’95 [2], vol. 1, p. 267–272, 1995.

    Google Scholar 

  230. M. Quafafou and M. Nafia. GAITS: Fuzzy set-based algorithms for computing strategies using genetic algorithms. In Proc. of the Int. Conf. on Fuzzy Logic in Artificial Intelligence, Linz (Austria), 28.–30. June 1993.

    Google Scholar 

  231. M. F. Ramalho and E. M. Scharf. Fuzzy logic tool and genetic algorithms for CAC in ATM networks. Electronics Letters, 32(11):973–974, 1996.

    Google Scholar 

  232. A. R. M. Ramos and D. A. Barone. Intelligent solutions for cybernetics vehicle control. In IEEE-SMC’95 [121], p. 2983–2987.

    Google Scholar 

  233. L. M. Rocha. Contextual genetic algorithms: evolving developmental rules. In Advances in Artificial Life. Proc. of the Third European Conf. on Artificial Life, vol. 929 of Lecture Notes in Artificial Intelligence, p. 368–382, Granada (Spain), 4.–6. June 1995. Springer-Verlag, Berlin.

    Google Scholar 

  234. T. Rubinson and R. Yager. Fuzzy logic and genetic algorithms for financial risk management. In Proc. of the IEEE/IAFE Computational Intelligence in Financial Engineering Conf., New York, 24.–26. Mar. 1996. IEEE, New York.

    Google Scholar 

  235. M. Sakawa, J. Utaka, M. Inuiguchi, I. Shiromaru, N. Suginohara, and T. Inoue. Hot parts operating schedule of gas turbines by genetic algorithms and fuzzy satisficing methods. In IJCNN’93 [123], p. 746–749.

    Google Scholar 

  236. M. Sakurai, Y. Kurihara, and S. Karasawa. Color classification using fuzzy inference and genetic algorithm. In FuzzyIEEE94 [80].

    Google Scholar 

  237. E. Sanchez. Genetic algorithms, neural networks and fuzzy logic systems. In Proc. of the 2nd Int. Conf. on Fuzzy Logic and Neural Networks (IIZUKA’92), vol. 1, p. 17–19, Iizuka (Japan), 17.–22. July 1992. Fuzzy Logic Systems Institute.

    Google Scholar 

  238. M. Sasaki, M. Gen, and M. Yamashiro. A method for solving fuzzy De Novo programming problem by genetic algorithms. Comput. Ind. Eng. (UK), 29:507–511, 1995.

    Google Scholar 

  239. A. Satyadas and K. KrishnaKumar. GA-optimized fuzzy controller for spacecraft attitude control. In FuzzyIEEE94 [80].

    Google Scholar 

  240. M. Schöder, F. Klawonn, and R. Kruse. Genetic algorithms and fuzzy situations for sequential optimization of control surfaces. In Proc. of the 3rd Int. Symposium on Uncertainty Modeling and Analysis and Annual Conf. of the North American Fuzzy Information Processing Society (ISUMA-NAFIPS’95), p. 777–781, College Park, MD, 17.–20. Sept. 1995. IEEE, New York.

    Google Scholar 

  241. C. M. Schulte. Genetic algorithms for prototype based fuzzy clustering. In EUFIT’94 [65], p. 913–921.

    Google Scholar 

  242. T. Shibata, T. Abe, K. Tanie, and M. Nose. Motion planning of a redundant manipulator-criteria of skilled operators by fuzzy-ID3 and GMDH and optimization by GA. In Proc. of 1995 IEEE Int. Conf. on Fuzzy Systems, vol. 5, p. 99–102, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, New York, NY.

    Google Scholar 

  243. T. Shibata and T. Fukuda. Coordination in evolutionary multi-agent-robotic system using fuzzy and genetic algorithm. Control Engineering Practice, 2(1):103–111, Jan. 1994.

    Google Scholar 

  244. T. Shibata, T. Fukuda, and K. Tanie. Fuzzy critic for robotic motion planning by genetic algorithm in hierarchical intelligent control. In IJCNN’93 [123], p. 770–773.

    Google Scholar 

  245. T. Shibata, T. Fukuda, and K. Tanie. Synthesis of fuzzy, artificial intelligence, neural networks, and genetic algorithm for hierarchical intelligent control. In IJCNN’93 [123], p. 2869–2872.

    Google Scholar 

  246. K. Shimojima. Unsupervised/supervised learning for RBF-fuzzy inference-adaptive rules and membership function and hierarchical structures by genetic algorithms. In AFLNNGA’94 [1]

    Google Scholar 

  247. K. Shimojima, T. Pukuda, and Y. Hasegawa. RBF-fuzzy system with GA based un-supervised/supervised learning method. In Proc. of the 4th IEEE Int. Conf. on Fuzzy Systems and the 2nd Int. Fuzzy Engineering Symposium, vol. 1, p. 253–258, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, New York.

    Google Scholar 

  248. K. Shimojima, T. Fukuda, and Y. Hasegawa. Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm. Fuzzy Sets and Systems, 72(3):295–309, 1995.

    Google Scholar 

  249. H. Shin and S.-O. Jo. The optimal model identification of a system from experimental data by a genetic algorithm. In Korea-Australia EC’95 [153], p. 91–113.

    Google Scholar 

  250. D. Simon and H. El-Sherief. Fuzzy phase-locked loops. In Proc. of the 1994 IEEE Position, Location, and Navigation Symposium, p. 252–259, Las Vegas, NV, 11.–15. Apr. 1994. IEEE, New York.

    Google Scholar 

  251. I. Šimoník, P. Ošmera, M. Pokorný, and D. Zbyszek. Using the genetic algorithms for fuzzy nonlinear regression. In P. Ošmera, ed., Proc. of the MENDEL’96, p. 160–163, Brno (Czech Republic), June 1996. Technical University of Brno.

    Google Scholar 

  252. C. K. Soh and J. Yang. Fuzzy controlled genetic algorithm search for shape optimization. Journal of Computing in Civil Engineering, 10(2):143–150, 1996.

    Google Scholar 

  253. R. Srikanth, R. George, D. Prabhu, and F. E. Petry. Fuzzy clustering using genetic algorithms. In Proc. of the 36th Midwest Symposium on Circuits and Systems, p. 1362–1365, Detroit, Michigan, 16.–18. Aug. 1993. IEEE, New York.

    Google Scholar 

  254. R. J. Stonier. Adaptive learning using genetic algorithms and evolutionary programming in robotic systems. In Korea-Australia EC’95 [153], p. 183–198.

    Google Scholar 

  255. G. Strbac and P. Djapic. A genetic based fuzzy approach to optimisation of electrical distribution networks. In IEE/IEEE Sheffield ‘95 [122], p. 194–199.

    Google Scholar 

  256. B. Subudhi and A. K. Swain. Genetic algorithm based fuzzy logic controller for real time liquid level control. J. Inst. Eng. (India) Electr. Eng. Div., 76:96–100, Aug. 1995.

    Google Scholar 

  257. M. Sugeno and S.-H. Kwon. New approach to time series modeling with fuzzy measures and the choquet integral. In Proc. of the 1995 IEEE Int. Conf. on Fuzzy Systems, vol. 2, p. 799–804, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  258. C.-T. Sun and M.-D. Wu. Multi-stage genetic algorithm learning in game playing. In Proc. of the First Int. Joint Conf. of the North American Fuzzy Information Processing Society Biannual Conf.. The Industrial Fuzzy Control and Intelligent Systems Conf., and The NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic, p. 223–227, San Antonio, TX, 18.–21. Dec. 1994. IEEE, New York.

    Google Scholar 

  259. H. Surmann, J. Huser, and L. Peters. Fuzzy system for indoor mobile robot navigation. In Proc. of the 1995 IEEE Int. Conf. on Fuzzy Systems, vol. 5, p. 71–76, Yokohama (Japan), 20.–24. Mar. 1995. IEEE, Piscataway, NJ.

    Google Scholar 

  260. T. Suzuki, K. Shida, H. Fujikawa, and S. ichi Yamada. A design method of MRACS with fuzzy adaptive control rules using genetic algorithms. In Proc. of the 19th Annual Conf. of IEEE Industrial Electronic Society (IECON’93), vol. 3, p. 2288–2292, Maui, HI, Nov. 1993. IEEE Press, New York.

    Google Scholar 

  261. H.-M. Tai and S. Shenoi. Robust fuzzy controllers. In Proc. of the 1994 IEEE Int. Conf. on Systems, Man, and Cybernetics, vol. 1, p. 85–90, San Antonio, TX, 2.–5. Oct. 1994. IEEE, New York.

    Google Scholar 

  262. H. Takagi. Fusion techniques of fuzzy-systems and neural networks, and fuzzy-systems and genetic algorithms. In B. Bosacchi and J. C. Bezdek, eds., Applications of Fuzzy Logic Technology, vol. SPIE-2061, p. 402–413, Boston, MA, 8.–10. Sept. 1993. The Int. Society for Optical Engineering.

    Google Scholar 

  263. H. Takagi. Neural networks and genetic algorithm techniques for fuzzy systems. In Proc. of the World Congress on Neural Networks-WCNN ‘93, p. 631–634, Portland, OR, 11.–15. July 1993. IEEE.

    Google Scholar 

  264. K. Tanaka and K. Yoshioka. Fuzzy trajectory control and G A-based obstacle avoidance of a truck with five trailers. In IEEE-SMC’95 [121], p. 4378–4382.

    Google Scholar 

  265. M. Tanaka, J. Ye, and T. Taniko. Fuzzy modelling by genetic algorithm with tree-structured individuals. Int. Journal of Systems Engineering, 27(2):261–268, 1996.

    MATH  Google Scholar 

  266. K. S. Tang, K. F. Man, and C. Y. Chan. Fuzzy control of water pressure using genetic algorithm. In Proc. of the Safety, Reliability and Applications of Emerging Intelligent Control Technologies, p. 15–20, Hong Kong, 12.–14. Dec. 1995. Pergamon, Oxford (UK).

    Google Scholar 

  267. Y. S. Tarng, C.-Y. Lin, and C. Y. Nian. Automatic generation of a fuzzy rule base for constant turning force. J. Intell. Manuf., 7(1):77–84, 1996.

    Google Scholar 

  268. W. Tautz. Genetic algorithms for designing fuzzy systems. In EUFIT’94 [65], p. 558–567.

    Google Scholar 

  269. P. Thrift. Fuzzy logic synthesis with genetic algorithms. In L. B. Booker, editors. Proc. of the Fourth Int. Conf. on Genetic Algorithms, San Diego, 13.–16. July 1991. Morgan Kaufmann Publishers Belew and Booker [19], p. 509–513.

    Google Scholar 

  270. P. Törmänen. Adaptive fuzzy Pi-controller optimization by GA: A simulation study. In Alander [9], p. 287–288. (ftp: ftp.uwasa.fi: cs/2NWGA/Tormanen.ps.Z).

    Google Scholar 

  271. T. Toshikawa, T. Furuhashi, and Y. Uchikawa. Emergence of fuzzy control rules from DNA. In Proc. of the 1995 IEEE/Nagaoya University World Wisepersons Workshop (WWW95) on Fuzzy Logic and Neural Networks/Evolutionary Computation, p. 49–55, Nagoya, Japan, 14.–15. Nov. 1995. Nagoya Univ. (Nagoya, Japan).

    Google Scholar 

  272. T. Tsuchiya, T. Maeda, Y. Matsubara, and M. Nagamachi. A fuzzy rule induction method using genetic algorithm. Int. Journal of Industrial Ergonomics, 18(2–3):135–146, Sept. 1996.

    Google Scholar 

  273. M. Valenzuela-Rendón. The fuzzy classifier system: A classifier system for continuously varying variables. In L. B. Booker, editors. Proc. of the Fourth Int. Conf. on Genetic Algorithms, San Diego, 13.–16. July 1991. Morgan Kaufmann Publishers Belew and Booker [19], p. 346–353.

    Google Scholar 

  274. J. R. Velasco, G. Fernández, and L. Magdalena. Inductive learning applied to fossil power plants control optimization. In Proc. of the IFAC Symposium on Control of Power Plants and Power Systems, p. 205–210, Munich (Germany), 9.–11. Mar. 1992. Pergamon Press Inc, Tarrytown, NY.

    Google Scholar 

  275. J. R. Velasco and L. Magdalena. Genetic algorithms in fuzzy control systems. In J. Périaux, M. Galán, and P. Cuesta, editors. Genetic Algorithms in Engineering and Computer Science (EUROGEN95), Las Palmas (Spain), Dec. 1995. John Wiley & Sons, New York Winter et al. [286], p. 141–165.

    Google Scholar 

  276. J. J. Vidal and M. G. Cooper. Genetic design of fuzzy controllers: the cart and jointed-pole problem. In FuzzyIEEE94 [80].

    Google Scholar 

  277. H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors. Parallel Problem Solving from Nature-PPSN IV, vol. 1141 of Lecture Notes in Computer Science, Berlin (Germany), 22.–26. Sept. 1996. Springer-Verlag, Berlin.

    Google Scholar 

  278. H.-M. Voigt, H. Mühlenbein, and D. Cvetkovic. Fuzzy recombination for the continuous breeder genetic algorithm. In Eshelman [63].

    Google Scholar 

  279. L. Wang, L. Zhang, H. Itoh, and H. Seki. An acquisition method of fuzzy classification rules based on genetic algorithm. In Proc. of the 3rd Pecific Rim Int. Conf. on Artificial Intelligence, vol. 1, p. 403–408, Beijing (China), 15.–18. Aug. 1994. Princeton University Press, Princeton.

    Google Scholar 

  280. P. Wang and D. P. Kwok. Optimal fuzzy PID control based on genetic algorithms. In Proc. of the 1992 Int. Conf. on Industrial Electronics, Control, and Instrumentation, vol. 2, p. 977–981, San Diego, 9.–13. Nov. 1992. IEEE Press.

    Google Scholar 

  281. Proc. of the Second Online Workshop on Evolutionary Computation (WEC2), Nagoya (Japan), 4.–22. Mar. 1996.

    Google Scholar 

  282. S. Welstead. Financial data modeling with genetically optimized fuzzy systems. In Proc. of the Second Annual Int. Conf. on Artificial Intelligence Applications on Wall Street: Tactical and Strategic Computing Technologies, p. 286–293, New York, NY, 19.–22. Apr. 1993. Software Engineering Press, Gaithersburg, MD.

    Google Scholar 

  283. R. Wiggins. Docking a truck: A genetic fuzzy approach. AI Expert, 7(5):28–35, May 1992.

    Google Scholar 

  284. P. Wilke, J. Rehder, G. Billing, C. Mansfeld, and J. Nilson. NeuroGraph-a simulation environment for neural networks, genetic algorithms and fuzzy logic. In D. W. Pearson, N. C. Steele, and R. F. Albrecht, eds., Artificial Neural Nets and Genetic Algorithms, p. 515–518, Alès (France), 19.–21. Apr. 1995. Springer-Verlag, Wien New York.

    Google Scholar 

  285. T. Williams. Fuzzy, neural and genetic methods train to overcome complexity. Computer Design, 34(5):59–76, May 1995.

    Google Scholar 

  286. G. Winter, J. Périaux, M. Galán, and P. Cuesta, editors. Genetic Algorithms in Engineering and Computer Science (EUROGEN95), Las Palmas (Spain), Dec. 1995. John Wiley & Sons, New York.

    Google Scholar 

  287. T. Wolf. Optimization of fuzzy systems using neural networks and genetic algorithms. In EUFIT’94 [65], p. 544–551.

    Google Scholar 

  288. C.-C. Wong, M.-C. Su, and N.-S. Lin. Fuzzy rule extraction for controller designs. In Proc. of the 1995 Int. IEEE/IAS Conf., on Industrial Automation and Control: Emerging Technologies, p. 409–412, Taipei (Taiwan), 22.–27. May 1995. IEEE.

    Google Scholar 

  289. K. P. Wong and S. Y. W. Wong. Combined genetic algorithm/simulated annealing/fuzzy set approach to short-term generation scheduling with take-or-pay fuel contract. IEEE Trans. on Power Systems, 11(1):128–135, Feb. 1996. (1995 IEEE Power Industry Computer Application Conf. (PICA 95)).

    Google Scholar 

  290. J. C. Wu and T. S. Liu. Fuzzy control of rider-motorcycle system using genetic algorithm and auto-tuning. Mechatronics, 5(4):441–455, June 1995.

    Google Scholar 

  291. W. Xie, W. Li, and X. Gao. Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning. In Proc. of the Int. Conf. on Intelligent Manufacturing, vol. 2620, p. 493–498, Wuhan, China, 10. June 1995. SPIE-Society of Photo-Optical Instrumantation Engineering, Bellingham, WA (USA).

    Google Scholar 

  292. H. Y. Xu and G. Vukovich. A fuzzy genetic algorithm with effective search and optimization. In IJCNN’93 [123], p. 2967–2970.

    Google Scholar 

  293. H. Y. Xu and G. Vukovich. Fuzzy evolutionary algorithms and automatic robot trajectory generation. In ICEC’94 [119], p. 595–600.

    Google Scholar 

  294. H. Xue, N. Chong, and M. Jamshidi. Fuzzy associative memory optimization using genetic algorithms. In FuzzyIEEE94 [80].

    Google Scholar 

  295. J. Yang and C. K. Soh. An integrated shape optimization approach using genetic algorithms and fuzzy rule-based system. In Proc. of the Developments in Neural Networks and Evolutionary Computing for Civil and Structural Engineering, p. 171–177, Cambridge, UK, 28.–30. Aug. 1995. Civil Comp. Press, Edingburgh.

    Google Scholar 

  296. T. Yoshikawa, T. Furuhashi, and Y. Uchikawa. A fuzzy modeling of very large scale system using genetic algorithm and a multiple-representing method. In FuzzyIEEE94 [80].

    Google Scholar 

  297. S. Zeng and Y. He. Learning and tuning fuzzy logic controllers through genetic algorithm. In Proc. of ICCI94/Neural Networks, Orlando, FL, 26. June–2. July 1994. IEEE, New York, NY.

    Google Scholar 

  298. L. Zhang, Y. Li, and H. Chen. A new global optimizing algorithm for fuzzy neural networks. Int. Journal of Electronics, 80(3):393–403, Mar. 1996.

    Google Scholar 

  299. Y.-S. Zhou and L.-Y. Lai. Optimal design of fuzzy controllers by genetic algorithms. In Proc. of the 1995 Int. IEEE/IAS Conf. on Industrial Automation and Control: Emerging Technologies, p. 429–435, Taipei (Taiwan), 22.–27. May 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Vaasa, P.O. Box 700, FIN-65101, Vaasa, Finland

    Jarmo T. Alander

Authors
  1. Jarmo T. Alander
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The University of Manitoba, Winnipeg, Manitoba, Canada

    Witold Pedrycz

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer Science+Business Media New York

About this chapter

Cite this chapter

Alander, J.T. (1997). An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic. In: Pedrycz, W. (eds) Fuzzy Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6135-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-6135-4_13

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7811-2

  • Online ISBN: 978-1-4615-6135-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation