Hisao Ishibuchi

Hisao Ishibuchi received the B.S. and M.S. degrees from Kyoto University, Japan, in 1985 and 1987, respectively. He received the Ph.D. degree from Osaka Prefecture University, Japan, in 1992. Since 1987, he has been with Osaka Prefecture University where he is currently a Professor at Department of Computer Science and Intelligent Systems. He is also the Head of Computational Intelligence Research Center of Osaka Prefecture University. He was a Visiting Researcher in University of Toronto (1994-1995 and 1997- 1998). He received the GECCO 2004 Best Paper Award in the genetic algorithms track (June 26–30, 2004, Seattle), the 2006 Outstanding Book Award from SOFT, and the HIS-NCEI 2006 Best Paper Award (December 13–15, 2006, New Zealand). He has published more than 60 international journal papers, 300 international conference papers, and 30 book chapters. He is a Coauthor of the two books: Soft Data Analysis, 1995, and Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining, 2004. He is an Associate Editor of IEEE Computational Intelligence Magazine, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Systems, Man and Cybernetics Part B, International Journal of Computational Intelligence Research, and Mathware and Soft Computing. He is also an Area Editor of Soft Computing Journal, a Vice Chairman of the fuzzy technical committee of IEEE CI Society, and a Vice President of SOFT. His current research interests include fuzzy rule-based systems, genetic fuzzy systems, evolutionary multiobjective optimization, fuzzy data mining, and evolutionary games.

Biography Updated on 1 May 2007

Articles in Scholarly Journals [Incomplete List]

  1. A genetic approach to the design of autonomous agents for futures trading
    Artificial Life and Robotics, vol. 11, no. 2, pp. 145–148, 2007
  2. A weighted fuzzy classifier and its application to image processing tasks
    Fuzzy Sets and Systems, vol. 158, no. 3, pp. 284–294, 2007
  3. Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning
    International Journal of Approximate Reasoning, vol. 44, no. 1, pp. 4–31, 2007
  4. Special Issue on Memetic Algorithms
    IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 37, no. 1, pp. 2–5, 2007
  5. An approach to fuzzy default reasoning for function approximation
    Soft Computing, vol. 10, no. 9, pp. 850–864, 2005
  6. Evolution of Iterated Prisoner's Dilemma Game Strategies in Structured Demes Under Random Pairing in Game Playing
    IEEE Transactions on Evolutionary Computation, vol. 9, no. 6, pp. 552–561, 2005
  7. Rule Weight Specification in Fuzzy Rule-Based Classification Systems
    IEEE Transactions on Fuzzy Systems, vol. 13, no. 4, pp. 428–435, 2005
  8. Hybridization of Fuzzy GBML Approaches for Pattern Classification Problems
    IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 35, no. 2, pp. 359–365, 2005
  9. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining
    Fuzzy Sets and Systems, vol. 141, no. 1, pp. 59–88, 2004
  10. Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems
    Fuzzy Optimization and Decision Making, vol. 3, no. 2, pp. 119–139, 2004
  11. Learning fuzzy rules from iterative execution of games
    Fuzzy Sets and Systems, vol. 135, no. 2, pp. 213–240, 2003
  12. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling
    IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 204–223, 2003
  13. Effect of rule weights in fuzzy rule-based classification systems
    IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, pp. 506–515, 2001
  14. Evolution of unplanned coordination in a market selection game
    IEEE Transactions on Evolutionary Computation, vol. 5, no. 5, pp. 524–534, 2001
  15. Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
    Fuzzy Sets and Systems, vol. 119, no. 2, pp. 273–290, 2001
  16. Numerical analysis of the learning of fuzzified neural networks from fuzzy if–then rules
    Fuzzy Sets and Systems, vol. 120, no. 2, pp. 281–307, 2001
  17. Three-objective genetics-based machine learning for linguistic rule extraction
    Information Sciences, vol. 136, no. 1-4, pp. 109–133, 2001
  18. Neural networks for soft decision making
    Fuzzy Sets and Systems, vol. 115, no. 1, pp. 121–140, 2000
  19. Voting in fuzzy rule-based systems for pattern classification problems
    Fuzzy Sets and Systems, vol. 103, no. 2, pp. 223–238, 1999
  20. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems
    IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 29, no. 5, pp. 601–618, 1999
  21. Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes
    IEEE Transactions on Industrial Electronics, vol. 46, no. 6, pp. 1057–1068, 1999
  22. Multi-objective scheduling with fuzzy due-date
    Computers & Industrial Engineering, vol. 35, no. 3-4, pp. 439–442, 1998
  23. Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms
    Computers & Industrial Engineering, vol. 35, no. 3-4, pp. 575–578, 1998
  24. A multi-objective genetic local search algorithm and its application to flowshop scheduling
    IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol. 28, no. 3, pp. 392–403, 1998
  25. Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems
    Fuzzy Sets and Systems, vol. 89, no. 2, pp. 135–150, 1997
  26. Learning of fuzzy classification rules by a genetic algorithm
    Electronics and Communications in Japan (Part III: Fundamental Electronic Science), vol. 80, no. 3, pp. 37–46, 1997
  27. Comparison of the Michigan and Pittsburgh approaches to the design of fuzzy classification systems
    Electronics and Communications in Japan (Part III: Fundamental Electronic Science), vol. 80, no. 12, pp. 10–19, 1997
  28. A simple but powerful heuristic method for generating fuzzy rules from numerical data
    Fuzzy Sets and Systems, vol. 86, no. 3, pp. 251–270, 1997
  29. Multi-objective genetic algorithm and its applications to flowshop scheduling
    Computers & Industrial Engineering, vol. 30, no. 4, pp. 957–968, 1996
  30. Genetic algorithms for flowshop scheduling problems
    Computers & Industrial Engineering, vol. 30, no. 4, pp. 1061–1071, 1996
  31. Adaptive fuzzy rule-based classification systems
    IEEE Transactions on Fuzzy Systems, vol. 4, no. 3, pp. 238–250, 1996
  32. Selecting fuzzy if-then rules for classification problems using genetic algorithms
    IEEE Transactions on Fuzzy Systems, vol. 3, no. 3, pp. 260–270, 1995
  33. Exponential possibility regression analysis
    Fuzzy Sets and Systems, vol. 69, no. 3, pp. 305–318, 1995
  34. A learning algorithm of fuzzy neural networks with triangular fuzzy weights
    Fuzzy Sets and Systems, vol. 71, no. 3, pp. 277–293, 1995
  35. Modified simulated annealing algorithms for the flow shop sequencing problem
    European Journal of Operational Research, vol. 81, no. 2, pp. 388–398, 1995
  36. Local search algorithms for flow shop scheduling with fuzzy due-dates
    International Journal of Production Economics, vol. 33, no. 1-3, pp. 53–66, 1994
  37. Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms
    Fuzzy Sets and Systems, vol. 65, no. 2-3, pp. 237–253, 1994
  38. Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems
    Fuzzy Sets and Systems, vol. 67, no. 1, pp. 81–100, 1994
  39. Empirical study on learning in fuzzy systems by rice taste analysis
    Fuzzy Sets and Systems, vol. 64, no. 2, pp. 129–144, 1994
  40. Neural networks that learn from fuzzy if-then rules
    IEEE Transactions on Fuzzy Systems, vol. 1, no. 2, pp. 85–97, 1993
  41. An architecture of neural networks with interval weights and its application to fuzzy regression analysis
    Fuzzy Sets and Systems, vol. 57, no. 1, pp. 27–39, 1993
  42. Identification method of possibility distributions and its application to discriminant analysis
    Fuzzy Sets and Systems, vol. 58, no. 1, pp. 41–50, 1993
  43. Efficient fuzzy partition of pattern space for classification problems
    Fuzzy Sets and Systems, vol. 59, no. 3, pp. 295–304, 1993
  44. Multiobjective programming in optimization of the interval objective function
    European Journal of Operational Research, vol. 48, no. 2, pp. 219–225, 1990
  45. Identification of fuzzy parameters by interval regression models
    Electronics and Communications in Japan (Part III: Fundamental Electronic Science), vol. 73, no. 12, pp. 19–27, 1990