Citation Report (Last Update: 22/12/2009)
Author = (Ishibuchi H*) AND Address = (Univ Osaka Prefecture) OR (Osaka Prefecture Univ)
Timespan = All Years. Databases = SCI-EXPANDED

  1. 275 Citations
    H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka: Selecting Fuzzy If-Then Rules for Classification Problems Using Genetic Algorithms, IEEE Transactions on Fuzzy Systems, Vol. 3, No. 3, pp. 260-270, 1995.
  2. 221 Citations
    H. Ishibuchi, T. Murata: A Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Transactions on Systems, Man, and Cybernetics - Part C, Vol. 28, No. 3, pp. 392-403, 1998.
  3. 181 Citations
    H. Ishibuchi, K. Nozaki, H. Tanaka: Distributed Representation of Fuzzy Rules and Its Application to Pattern Classification, Fuzzy Sets and Systems, Vol. 52, No. 1, pp. 21-32, 1992.
  4. 170 Citations
    H. Ishibuchi, T. Nakashima, T. Murata: Performance Evaluation of Fuzzy Classifier Systems for Multi-Dimensional Pattern Classification Problems, IEEE Transactions on Systems, Man, and Cybernetics- Part B, Vol. 29, No. 5, pp. 601-618, 1999.
  5. 133 Citations
    H. Ishibuchi, T. Yoshida, T. Murata: 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. PDF
  6. 120 Citations
    K. Nozaki, H. Ishibuchi, H. Tanaka: 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.
  7. 118 Citations
    T. Murata, H. Ishibuchi, H. Tanaka: Genetic Algorithms for Flowshop Scheduling Problems, Computers and Industrial Engineering, Vol. 30, No. 4, pp. 1061-1071, 1996.
  8. 115 Citations
    T. Murata, H. Ishibuchi, H. Tanaka: Multi-Objective Genetic Algorithm and Its Application to Flowshop Scheduling, Computers and Industrial Engineering, Vol. 30, No. 4, pp. 957-968, 1996.
  9. 108 Citations
    H. Ishibuchi, T. Nakashima: Effect of Rule Weights in Fuzzy Rule-Based Classification Systems, IEEE Transactions on Fuzzy Systems, Vol. 9, No. 4, pp. 506-515, 2001. PDF
  10. 108 Citations
    H. Ishibuchi, T. Murata, I.B.Turksen: 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.
  11. 92 Citations
    H. Ishibuchi, T. Nakashima, T. Murata: Three-Objective Genetics-Based Machine Learning for Linguistic Rule Extraction, Information Sciences, Vol.136, No.1-4, pp. 109-133, 2001. PDF

  12. 87 Citations
    H. Ishibuchi, T. Nakashima, T. Morisawa: Voting in Fuzzy Rule-based Systems for Pattern Classification Problems, Fuzzy Sets and Systems, Vol. 103, No. 2, pp. 223-238, 1999.
  13. 86 Citations
    K. Nozaki, H. Ishibuchi, H. Tanaka: Adaptive Fuzzy Rule-Based Classification Systems, IEEE Transactions onf Fuzzy Systems, Vol. 4, No. 3, pp. 238-250, 1996.
  14. 85 Citations
    H. Ishibuchi, T. Yamamoto: 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. PDF

  15. 61 Citations
    H. Ishibuchi, K. Kwon, H. Tanaka: A Learning Algorithm of Fuzzy Neural Networks with Triangular Fuzzy Weights, Fuzzy Sets and Systems, Vol. 71, No. 3, pp. 277-293, 1995.
  16. 59 Citations
    H. Ishibuchi, K. Nozaki, N. Yamamoto: 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.
  17. 57 Citations
    H. Ishibuchi, T. Yamamoto Rule Weight Specification in Fuzzy Rule-Based Classification Systems , IEEE TRANSACTIONS ON FUZZY SYSTEMS, Vol. 13, No. 4, pp. 428-435, 2005.
  18. 57 Citations
    H. Tanaka, H. Ishibuchi: Identification of Possibilistic Linear-Systems by Quadratic Membership Functions of Fuzzy Parameters, Fuzzy Sets and Systems, Vol. 41, No. 2, pp. 145-160, 1991.
  19. 54 Citations
    H. Ishibuchi, N. Yamamoto, T. Murata: Genetic Algorithms and Neighborhood Search Algorithms for Fuzzy Flowshop Scheduling Problems, Fuzzy Sets and Systems, Vol. 67, No. 1, pp. 81-100, 1994.
  20. 46 Citations
    H. Ishibuchi, H. Tanaka: Fuzzy Regression-Analysis Using Neural Networks, Fuzzy Sets and Systems, Vol. 50, No. 3, pp. 257-265, 1992.
  21. 39 Citations
    H. Tanaka, H. Ishibuchi, S. Yoshikawa: Exponential Possibility Regression-Analtsis , Fuzzy Sets and Systems, Vol. 69, No. 3, pp. 305-318, 1995.
  22. 38 Citations
    H. Ishibuchi, T. Nakashima: 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.
  23. 38 Citations
    H, Ishibuchi, H. Tanaka, H. Okada: 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.
  24. 37 Citations
    H. Ishibuchi, Y. Nojima: Analysis of Interpretability-Accuracy Tradeoff of Fuzzy Systems by Multiobjective Fuzzy Genetics-Based Machine Learning, International Journalof Approximate Reasnoning, Vol. 44, No. 1, pp. 4-31, 2007. PDF

  25. 32 Citations
    T. Murata, H. Ishibuchi, Gen M: Specification of Genetic Search Directions in Cellular Multi-Objective Genetic Algorithms, Evolutionary Multi-Criterion Optimization, Proceedings, Vol. 1993, pp. 82-95, 2001.