1. H. Ishibuchi, T. Nakashima, M. Nii: Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining, pp.1-308, Springer, Berlin, November 2004.





  1. H.Tanaka and H.Ishibuchi: Possibilistic Regression Analysis Based on Linear Programming, in J.Kacprzyk and M.Fedrizzi (eds.): Fuzzy Regression Analysis, Omnitech Press, Warsaw, Poland, pp.47-60 (1992).

  2. H.Tanaka, H.Ishibuchi and T.Shigenaga: Fuzzy Inference System Based on Rough Sets and Its Application to Medical Diagnosis, in R.Slowinski (ed.): Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht, Netherlands, pp.111-117 (1992).




  3. H. Ishibuchi, S. Misaki, H. Tanaka: Simulated Annealing with Modified Generation Mechanism for Flow Shop Scheduling Problems, in T. Takamori and K. Tsuchiya (eds.): Robotics, Mechatronics and Manufacturing Systems, North-Holland, pp.809-814 (1993).

  4. H.Ishibuchi and H.Tanaka: Approximate Pattern Classification Using Neural Networks, in R.Lowen and M.Roubens (eds.): Fuzzy Logic: State of the Art, Kluwer Academic Publishers, Dordrecht, Netherlands, pp.225-236 (1993).




  5. H.Ishibuchi, H.Tanaka and S.Misaki: Fuzzy Flow Shop Scheduling by Simulated Annealing, in M.Delgado, J.Kacprzyk, J.-L.Verdegay and M.A.Vila (eds.): Fuzzy Optimization, Physica-Verlag, Heidelberg, Germany, pp.351-363 (1994).




  6. H.Ishibuchi: Development of Fuzzy Neural Networks, in W.Pedrycz (ed.): Fuzzy Modelling: Paradigms and Practice, Kluwer Academic Publishers, Boston, USA, pp.185-202 (1996).

  7. H.Ishibuchi, T.Murata and H.Tanaka: Construction of Fuzzy Classification Systems with Linguistic If-Then Rules Using Genetic Algorithms, in S.K.Pal and P.P.Wang (eds.): Genetic Algorithms for Pattern Recognition, CRC Press, Boca Raton, USA, pp.227-251 (1996).

  8. H.Ishibuchi and T.Murata: A Genetic-Algorithm-Based Fuzzy Partition Method for Pattern Classification Problems, in F.Herrera and J.L.Verdegay (eds.): Genetic Algorithm and Soft Computing, Physica-Verlag, Heidelberg, Germany, pp.555-578 (1996).




  9. H.Ishibuchi, T.Murata and T.Nakashima: Genetic-Algorithm-Based Approaches to Classification Problems, in W.Pedrycz (ed.): Fuzzy Evolutionary Computation, Kluwer AcademicPublishers, Boston, USA, pp.127-154 (1997).




  10. H.Ishibuchi and M.Nii: Fuzzy Neural Networks Techniques and Their Applications, in C.T.Leondes (ed.): Fuzzy Logic and Expert Systems Applications, Academic Press, San Diego, USA, pp.1-56 (1998).




  11. H.Ishibuchi, T.Nakashima, and T.Murata: Techniques and Applications of Genetic Algorithm-Based Methods for Designing Compact Fuzzy Classification Systems, in C. T. Leondes (ed.) Fuzzy Theory Systems: Techniques and Applications (Vol.3), Academic Press, San Diego, USA, 1999, Chapter 40, pp.1081-1109.

  12. H.Ishibuchi and M.Nii: Techniques and Applications of Neural Networks for Fuzzy Rule Approximation, in C. T. Leondes (ed.) Fuzzy Theory Systems: Techniques and Applications (Vol.4), Academic Press, San Diego, USA, 1999, Chapter 51, pp.1491-1519.




  13. H.Ishibuchi and T.Murata: Flowshop Scheduling with Fuzzy Duedate and Fuzzy Processing Time, in R. Slowinski and M.Hapke (eds.) Scheduling Under Fuzziness, Physica-Verlag, Heidelberg, Germany, 2000, Chapter 6, pp.113-143.

  14. H.Ishibuchi, T.Nakashima, and M.Nii: Fuzzy If-Then Rules for Pattern Classification, in D. Ruan and E. E. Kerre (eds.) Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications, Kluwer Academic Publishers, Boston, 2000, Chapter 12, pp.267- 295.




  15. H.Ishibuchi, T.Nakashima, and M.Nii: Genetic-Algorithm-Based Instance and Feature Selection, in H. Liu and H. Motoda (eds.) Instance Selection and Construction for Data Mining, Kluwer Academic Publishers, Boston, 2001, Chapter 6, pp.95-112.

  16. H.Ishibuchi and M.Nii: Minimizing the Measurement Cost in the Classification of New Samples by Neural-Network-Based Classifiers, in N. R. Pal (ed.) Pattern Recognition in Soft Computing Paradigm, World Scientific Publishers, Singapore, 2001, Chapter 10, pp.225-248.

  17. H.Ishibuchi and T.Nakashima: Fuzzy Rule-Based Strategy for a Market Selection Game, in N. Baba and L. Jain (eds.) Computational Intelligence in Games, Physica-Verlag, Heidelberg, Germany, 2001, Chapter 6, pp.133-156.

  18. H.Ishibuchi, M.Nii, and T.Nakashima: Approaches to the Design of Classification Systems from Numerical Data and Linguistic Knowledge, L. Ding (ed.) A New Paradigm of Knowledge Engineering by Soft Computing, World Scientific Publishers, Singapore, 2001, Chapter 12, pp.241-271.




  19. H.Ishibuchi and M.Nii: Fuzzification of Neural Networks for Classification Problems, in H. Bunke and A. Kandel (eds.) Hybrid Methods in Pattern Recognition, World Scientific Publishers, Singapore, 2002, Chapter 1, pp.1-31.

  20. H.Ishibuchi, R.Sakamoto, and T. Nakashima: Online Adaptation of Intelligent Decision-Making Systems, in C. T. Leondes (eds.) Intelligent Systems: Technology and Applications, Volume 1: Implementation Techniques, CRC Press, Boca Raton, 2002, Chapter 4, pp.87-113.

  21. H.Ishibuchi and T.Yamamoto: Comparison of Fuzzy Rule Selection Criteria for Classification Problems, in A. Abraham et al. (eds.) Soft Computing Systems: Design, Management and Applications: Frontiers in Artificial Intelligence and Applications (Vol. 87), 2002, pp.132-141.

  22. H.Ishibuchi and T.Yoshida: Hybrid Evolutionary Multi-Objective Optimization Algorithms, in A. Abraham et al. (eds.) Soft Computing Systems: Design, Management and Applications: Frontiers in Artificial Intelligence and Applications (Vol. 87), 2002, pp.163-172.




  23. H.Ishibuchi and T.Yamamoto: Trade-off between the Number of Fuzzy Rules and Their Classification Performance, in J.Casillas, O.Cordon, F.Herrera and L.Magdalena (eds.), Accuracy Improvements in Linguistic Fuzzy Modeling, pp.72-99, Springer-Verlag, Heidelberg, 2003.

  24. H.Ishibuchi and S.Kaige: A Simple but Powerful Multiobjective Hybrid Genetic Algorithms, in A.Abraham et al. (eds.): Design and Application of Hybrid Intelligent Systems (Frontiers in Artificial Intelligence and Applications, Vol.104), pp.244-251, 2003,December.




  25. T. Murata, S. Kaige, H. Ishibuchi: Local Search Direction for Multi-Objective Optimization Using Memetic EMO Algorithms, in Y. Jin (ed.): Knowledge Incorporation in Evolutionary Computation (Studies in Fuzziness and Soft Computing, Volume 167), pp.385-410, Springer-Verlag, Heidelberg, October 2004.

  26. H. Ishibuchi and Y. Shibata: Single-Objective and Multi-Objective Evolutionary Flowshop Scheduling, in C. A. Coello Coello and G. B. Lamont (eds.): Applications of Multi-Objective Evolutionary Algorithms, pp. 529-554 (Chapter 22), World Scientific, Singapore, October 2004.




  27. H. Ishibuchi and Y. Nojima: Fuzzy Ensemble Design through Multiobjective Fuzzy Rule Selection, in Y. Jin (ed.): Multi-Objective Machine Learning, pp. 507-530 Springer, Berlin April 2006.

  28. T. Nakashima and H. Ishibuchi: Computational Intelligence in RoboCup Soccer Simulation, in Gary Y. Yen and David B. Fogel (eds.) Computational Intelligence: Principles and Practice, pp.217-236, IEEE Computational Intelligence Society, 2006.






  1. H.Ishibuchi, T.Nakashima and T.Murata: A fuzzy classifier system that generates linguistic rules for pattern classificationproblems, Lecture Notes in Artificial Intelligence 1152: Fuzzy Logic, Neural Networks, and Evolutionary Computation, Springer-Verlag, Berlin, Germany, pp.35-54 (October 1996).




  2. T. Murata, H. Ishibuchi, T. Nakashima, M. Gen: Fuzzy partition and input selection by genetic algorithms for designing fuzzy rule-based classification systems, Lecture Notes in Computer Science 1447: Evolutionary Programming VII (7th International Conference on EP98, Sandiego, California, USA, March 25-27, 1998), pp.82-89, Springer, Berlin, November 1998.

  3. H.Ishibuchi and T.Nakashima: Evolution of reference sets in nearest neighbor classification, Lecture Notes in Artificial Intelligence 1585: Simulated Evolution and Learning (2nd Asia-Pacific Conference on Simulated Evolution and Learning, Canberra, 1998, Selected Papers), pp.82-89, Springer, Berlin, May 1999.




  4. K.Tanaka, M.Nii, and H.Ishibuchi: Learning from linguistic rules and rule extraction for function approximation by neural networks, Lecture Notes in Artificial Intelligence 1585: Simulated Evolution and Learning (2nd Asia-Pacific Conference on Simulated Evolution and Learning, Canberra, 1998, Selected Papers), pp.317-324, Springer, Berlin, May 1999.




  5. T. Murata, H. Ishibuchi, and M. Gen: Specification of local search directions in cellular multi-objective genetic algorithm, Lecture Notes in Computer Science 1993: Evolutionary Multi-Criterion Optimization, pp.82-95, Springer, Berlin, March 2001.

  6. H. Ishibuchi, T. Nakashima, and T. Murata: Multiobjective optimization in linguistic rule extraction from numerical data, Lecture Notes in Computer Science 1993: Evolutionary Multi-Criterion Optimization, pp.588-602, Springer, Berlin, March 2001.




  7. H. Ishibuchi, Y. Shibata: An empirical study on the effect of mating restriction on the search ability of EMO algorithms, Lecture Notes in Computer Science 2632: Evolutionary Multi-Criterion Optimization, pp.433-447, Springer, Berlin, April 2003.

  8. T. Murata, H. Nozawa, H. Ishibuchi, M. Gen: Modification of local search directions for non-dominated solutions in cellular multiobjective genetic algorithms for pattern classification problems, Lecture Notes in Computer Science 2632: Evolutionary Multi-Criterion Optimization, pp.593-607, Springer, Berlin, April 2003.

  9. H. Ishibuchi, T. Yamamoto: Effects of three-objective genetic rule selection on the generalization ability of fuzzy rule-based systems, Lecture Notes in Computer Science 2632: Evolutionary Multi-Criterion Optimization, pp.608-622, Springer, Berlin, April 2003.

  10. H. Ishibuchi, Y. Shibata: A similarity-based mating scheme for evolutionary multiobjective optimization, Lecture Notes in Computer Science 2723: Genetic and Evolutionary Computation Conference - GECCO 2003, pp.1065-1076, Springer, Berlin, July 2003.

  11. H. Ishibuchi, T. Yamamoto: Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers, Lecture Notes in Computer Science 2723: Genetic and Evolutionary Computation Conference - GECCO 2003, pp.1077-1088, Springer, Berlin, July 2003.

  12. T. Murata, S. Kaige, H. Ishibuchi: Generalization of dominance relation-based replacement rules for memetic EMO algorithms, Lecture Notes in Computer Science 2723: Genetic and Evolutionary Computation Conference - GECCO 2003, pp.1233-1244, Springer, Berlin, July 2003.

  13. T. Nakashima, M. Udo, and H. Ishibuchi: A Fuzzy Reinforcement Learning for a Ball Interception Problem, Lecture Notes in Artificial Intelligence 3020: RoboCup 2003: Robot Soccer World Cup VII, pp. 559-567, Springer, Berlin, July 2003.

  14. H.Ishibuchi and T.Yamamoto: Interpretability issues in fuzzy genetic-based machine learning for linguistic modelling, Lecture Notes in Artificial Intelligence 2873: Modelling with Words, pp.209-228, Springer, Berlin, December 2003.




  15. H. Ishibuchi, K. Narukawa: Some issues on the implementation of local search in evolutionary multiobjective optimization, Lecture Notes in Computer Science 3102: Genetic and Evolutionary Computation - GECCO 2004, pp.1246-1258, Springer, Berlin, June 2004.

  16. H. Ishibuchi, Y. Shibata: Mating scheme for controlling the diversity-convergence balance for multiobjective optimization, Lecture Notes in Computer Science 3102: Genetic and Evolutionary Computation - GECCO 2004, pp.1259-1271, Springer, Berlin, June 2004.

  17. T. Nakashima, H. Ishibuchi, A. Bargiela: A study on weighting training patterns for fuzzy rule-based classification systems, Lecture Notes in Artificial Intelligence 3131: Modeling Decisions for Artificial Intelligence, pp.60-69, Springer, Berlin, August 2004.

  18. H. Ishibuchi, S. Namba: Evolutionary multiobjective knowledge extraction for high-dimensional pattern classification problems, Lecture Notes in Computer Science 3242: Parallel Problem Solving from Nature - PPSN VIII, pp.1123-1132, Springer, Berlin, September 2004.




  19. H. Ishibuchi, K. Narukawa: Recombination of Similar Parents in EMO Algorithms, Lecture Notes in Computer Science 3410: Evolutionary Multi-Criterion Optimization - EMO 2005, pp.265-279, Springer, Berlin, March 2005.

  20. Y. Nojima, K. Narukawa, S. Kaige, H. Ishibuchi: Effects of Removing Overlapping Solutions on the Performance of the NSGA-II Algorithm, Lecture Notes in Computer Science 3410: Evolutionary Multi-Criterion Optimization - EMO 2005, pp.341-354, Springer, Berlin, March 2005.

  21. H. Ishibuchi, S. Kaige, K. Narukawa: Comparison between Lamarckian and Baldwinian Repair on Multiobjective 0/1 Knapsack Problems, Lecture Notes in Computer Science 3410: Evolutionary Multi-Criterion Optimization - EMO 2005, pp.370-385, Springer, Berlin, March 2005.




  22. T. Nakashima, M. Takatani, M. Udo, H. Ishibuchi, and M. Nii: Performance evaluation of an evolutionary method for RoboCup soccer strategies, Lecture Notes in Computer Science 4020: RoboCup 2005: Robot Soccer World Cup IX, pp. 616-623, Springer, Berlin, June 2006.

  23. H. Ishibuchi, T. Doi, and Y. Nojima: Incorporation of scalarizing fitness functions into evolutionary multiobjective optimization algorithms, Lecture Notes in Computer Science 4193: Parallel Problem Solving from Nature - PPSN IX, pp. 493-502, Springer, Berlin, September 2006.

  24. H. Ishibuchi, T. Doi, and Y. Nojima: Effects of using two neighborhood structures in cellular genetic algorithms for function optimization, Lecture Notes in Computer Science 4193: Parallel Problem Solving from Nature - PPSN IX, pp. 949-958, Springer, Berlin, September 2006.

  25. H. Ishibuchi, Y. Nojima, and I. Kuwajima: Finding simple fuzzy classification systems with high interpretability through multiobjective rule selection, Lecture Notes in Computer Science 4252: Knowledge-Based Intelligent Information and Engineering Systems - KES 2006, pp. 86-93, Springer, Berlin, 2006.




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