H.Ishibuchi, H.Tanaka, N.Fukuoka: Discriminant Analysis of Multi-Dimensional Interval Data and Its Application to Chemical Sensing, International J. of General Systems, Vol.16, No.4, pp.311-329 (1990, May).
H.Ishibuchi, H.Tanaka: Multiobjective Programming in Optimization of the Interval Objective Function, European J. of Operational Research, Vol.48, No.2, pp.219-225 (1990 ,Sep.).
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, May).
H.Ishibuchi, R.Fujioka, H.Tanaka: Possibility and Necessity Pattern Classification Using Neural Networks, Fuzzy Sets and Systems, Vol.48, No.3, pp.331-340 (1992, Jun.).
H.Tanaka, H.Ishibuchi, N.Matsuda: Fuzzy Expert System Based on Rough Sets and Its Application to Medical Diagnosis, International J. of General Systems, Vol.21, No.1, pp.83-97 (1992, Aug.).
H.Ishibuchi, H.Tanaka: Fuzzy Regression Analysis Using Neural Networks: Fuzzy Sets and Systems, Vol.50, No.3, pp.257-266 (1992, Sep.).
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, Nov.).
H.Tanaka, H.Ishibuchi: Evidence Theory of Exponential Possibility Distributions, International J. of Approximate Reasoning, Vol.8, No.2, pp.123-140 (1993, Mar.).
H.Ishibuchi, R.Fujioka, H.Tanaka: Neural Networks That Learn from Fuzzy If-Then Rules, IEEE Trans. on Fuzzy Systems, Vol.1, No.2, pp.85-97 (1993, May).
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, Jul.).
H.Tanaka, H.Ishibuchi, I.Hayashi: Identification Method of Possibility Distributions and Its Application to Discriminant Analysis, Fuzzy Sets and Systems, Vol.58, No.1, pp.41-50 (1993, Aug.).
H.Ishibuchi, K.Nozaki, H.Tanaka: Efficient Fuzzy Partition of Pattern Space for Classification Problems, Fuzzy Sets and Systems, Vol.59, No.3, pp.295-304 (1993, Nov.).
H.Ishibuchi, H.Tanaka, H.Okada: Interpolation of Fuzzy If-Then Rules by Neural Networks, International J. of Approximate Reasoning, Vol.10, No.1, pp.3-27 (1994, Jan.).
H.Ishibuchi, N.Yamamoto, S.Misaki, H.Tanaka: Local Search Algorithms for Flow Shop Scheduling with Fuzzy Due-Dates, International J. of Production Economics, Vol.33, No.1, pp.53-66 (1994, Jan.).
H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka: Selection of Fuzzy If-Then Rules by a Genetic Method, Electronics and Communications in Japan: Part III-Fundamental Electronic Science, Vol.77, No.2，pp. 94-104 (1994, Feb.).
K.Kwon, H.Ishibuchi, H.Tanaka: Neural Networks with Interval Weights for Nonlinear Mappings of Interval Vectors, IEICE Trans. on Information and Systems, Vol.E77-D, No.4, pp.409-417 (1994, Apr.).
H.Ishibuchi, K.Nozaki, H.Tanaka, Y.Hosaka, M.Matsuda: Empirical Study on Learning in Fuzzy Systems by Rice Taste Analysis, Fuzzy Set and Systems, Vol.64, No.2, pp.129-144 (1994, Jun.).
H.Ishibuchi, K.Nozaki, N.Yamamoto, H.Tanaka: 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, Aug.).
H.Ishibuchi, N.Yamamoto, T.Murata, H.Tanaka: Genetic Algorithms and Neighborhood Search Algorithms for Fuzzy Flowshop Scheduling Problems, Fuzzy Sets and Systems, Vol.67, No.1, pp.81-100 (1994, Oct.).
H.Ishibuchi, S.Misaki, H.Tanaka: Modified Simulated Annealing Algorithms for the Flow Shop Sequencing Problem, European J. of Operational Research, Vol.81, No.2, pp.388-398 (1995, Mar.).
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, May).
H.Ishibuchi, K.Nozaki, N.Yamamoto, H.Tanaka: Selecting Fuzzy If-Then Rules for Classification Problems Using Genetic Algorithms, IEEE Trans. on Fuzzy Systems, Vol.3, No.3, pp.260-270 (1995, Aug.).
H.Ishibuchi, K.Morioka, I.B.Turksen: Learning by Fuzzified Neural Networks, International J. of Approximate Reasoning, Vol.13, No.4, pp.327-358 (1995, Nov.).
T.Murata, H.Ishibuchi, H.Tanaka: Multi-Objective Genetic Algorithm and Its Application to Flowshop Scheduling, Computer and Industrial Engineering, Vol.30, No.4, pp.957-968 (1996, Oct.).
T.Murata, H.Ishibuchi, H.Tanaka: Genetic Algorithms for Flowshop Scheduling Problems, Computer and Industrial Engineering, Vol.30, No.4, pp.1061-1071 (1996, Oct.).
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, Mar.).
H.Ishibuchi, T.Murata: Learning of fuzzy classification rules by a genetic algorithm, Electronics and Communications in Japan (Part III: Fundamental Electronic Science), Vol.80, Issue 3, pp.37-46 (1997, Mar.).
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, Jul.).
H.Ishibuchi, T.Nakashima, T.Murata: 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, Issue 12, pp.10-19 (1997, Dec.).
H.Ishibuchi, T.Murata: A Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Trans. on Systems, Man, and Cybernetics -Part C: Applications and Reviews, vol.28, No.3(1998, Aug.).
T.Murata, M.Gen, H.Ishibuchi: Multi-Objective Scheduling with Fuzzy Due-Date, Computers and Industrial Engineering, Vol.35, No.3-4, pp.439-442 (1998, Dec.).
H.Ishibuchi, T.Murata, M.Gen: Performance Evaluation of Fuzzy Rule-Based Classification Systems Obtained by Multi-Objective Genetic Algorithms, Computers and Industrial Engineering, Vol.35, No.3-4, pp.575-578 (1998, Dec.).
H.Ishibuchi, M.Nii, K.Tanaka: Linguistic Rule Extraction from Neural Networks for High-Dimensional Classification Problems, COMPLEXITY INTERNATIONAL (On-line Journal: http://www.csu.edu.au/ci/), Vol.6 (1999, Jan.).
H.Ishibuchi, C.H.Oh, T.Nakashima: Compettition between Strategies for a Market Selection Game, COMPLEXITY INTERNATIONAL (On-line Journal: http://www.csu.edu.au/ci/), Vol.6 (1999, Jan.).
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, Apr.).
H.Ishibuchi, T.Nakashima, T.Murata: Performance Evaluation of Fuzzy Classifier Systems for Multi-Dimensional Pattern Classification Problems, IEEE Trans. on Systems, Man, and Cybernetics- Part B: Cybernetics, Vol. 29, No. 5, October, pp.601-618 (1999, Oct.).
H.Ishibuchi, T.Murata, T.Nakashima: Linguistic Rule Extraction from Numerical Data for High-Dimensional Classification Problems, International Journal of Advanced Computational Intelligence, Vol.3, No.5, pp.386-393 (1999, Oct.).
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, December, pp. pp.157-168 (1999, Dec.).
H.Ishibuchi, T.Nakashima: Pattern and Feature Selection by Genetic Algorithms in Nearest Neighbor Classification, International Journal of Advanced Computational Intelligence, Vol.4, No.2, pp.128-145 (2000, Feb.).
H.Ishibuchi, M.Nii: Neural Networks for Soft Decision Making, Fuzzy Sets and Systems, Vol.115,No.1, pp.121-140 (2000, Oct.).
H.Ishibuchi, M.Nii: Fuzzy Regression using Asymmetric Fuzzy Coefficients and Fuzzified Neural Networks, Fuzzy Sets and Systems, Vol.119, No.2, pp.273-290 (2001, Apr.).
H.Ishibuchi, M.Nii: Numerical Analysis of Learning of Fuzzified Neural Networks from Fuzzy If-Then Rules, Fuzzy Sets and Systems, Vol.120, No.2, pp.281-307 (2001, Jun.).
H.Ishibuchi, T.Nakashima, T.Murata: Three-Objective Genetics-Based Machine Learning for Linguistic Rule Extraction, Information Science, Vol.136, No.1-4, pp.109-133 (2001, Aug.).
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H.Ishibuchi, T.Nakashima: Effect of Rule Weights in Fuzzy Rule-Based Classification Systems, IEEE Trans. on Fuzzy Systems, Vol.9, No.4, pp.506-515 (2001, Aug.).
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H.Ishibuchi, T.Yamamoto: Effect of Fuzzy Discretization in Fuzzy Rule-Based Systems for Classification Problems with Continuous Attributes, Archives of Control Sciences, Vol.12, No.4, pp.351-378 (2002, Oct.).
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, Jan.).
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H.Ishibuchi, S.Kaige: Implementation of Simple Multiobjective Memetic Algorithms and Its Application to Knapsack Problems, International Journal of Hybrid Intelligent Systems, Vol.1, No.1, pp.22-35 (2004, Jan.).
H.Ishibuchi, T.Yamamoto: Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems, Fuzzy Optimization and Decision Making, Vol.3, No.2, pp.119-139 (2004, Jun.).
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H.Ishibuchi, T.Yamamoto, T.Nakashima: Hybridization of Fuzzy GBML Approaches for Pattern Classification Problems, IEEE Trans. on Systems, Man, and Cybernetics- Part B: Cybernetics, Vol.35, No.2, pp.359-365 (2005, Apr.).
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H.Ishibuchi, T.Yamamoto: Rule Weight Specification in Fuzzy Rule-Based Classification Systems, IEEE Trans. on Fuzzy Systems, Vol.13, No.4, pp.428-435 (2005, Aug.).
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H. Ishibuchi, N. Namikawa: Evolution of Iterated Prisoner's Dilemma Game Strategies in Structured Demes under Random Pairing in Game-Playing, IEEE Trans. on Evolutionary Computation, Vol.9, No.6, pp.552-561 (2005, Dec.).
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H. Ishibuchi, K. Narukawa, Y. Nojima: Handling of Overlapping Objective Vectors in Evolutionary Multiobjective Optimization, International Journal of Computational Intelligence Research, Vol.1, No.1, pp.1-18 (2005, Dec.).
H. Ishibuchi, T. Yamamoto, T. Nakashima: An Approach to Fuzzy Default Reasoning for Function Approximation, Soft Computing, Vol.10, No.9, pp.850-864 (2006, Jul.).
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H. Ishibuchi and Y. Nojima: Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers, International Journal of Hybrid Intelligent Systems, Vol.3, No.3, pp.129-145 (2006, Dec.).
H. Ishibuchi and Y. Nojima, "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, Jan.).
T. Nakashima, G. Schaefer, Y. Yokota, and H. Ishibuchi, "A weighted fuzzy classifier and its application to image processing tasks," Fuzzy Sets and Systems, vol. 158, no. 3, pp. 284-294 (2007, Feb.).
T. Nakashima, Y. Yokota, Y. Shoji, and H. Ishibuchi, "A genetic approach to the design of autonomous agents for futures trading," International Journal of Artificial Life and Robotics, vol. 11, no. 2, pp. 145-148 (2007, July.).
T. Nakashima, Y. Yokota, H. Ishibuchi, G. Schaefer, A. Drastich, and M Zavisek, "Constructing cost-sensitive fuzzy rule-based systems for pattern classification problems," Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 11, no. 6, pp. 546-553 (2007, July.).
Y. Nojima and H. Ishibuchi, "Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design," International Journal of Hybrid Intelligent Systems, vol. 4, no. 3, pp. 157-169 (2007, Oct.).
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).
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.
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.
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.
T. Murata, H. Ishibuchi, and M. Gen:
Specification of genetic 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
T. Nakashima, M. Takatani, M. Udo, H. Ishibuchi, 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.
H. Ishibuchi, T. Doi, 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.
H. Ishibuchi, T. Doi, 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.
H. Ishibuchi, Y. Nojima, 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.
H. Ishibuchi and Y. Nojima, "Optimization of scalarizing functions through evolutionary multiobjective optimization," Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, pp. 51-65, Springer, Berlin, 2007.
H. Ishibuchi, I. Kuwajima, and Y. Nojima, "Prescreening of candidate rules using association rule mining and Pareto-optimality in genetic rule selection," Lecture Notes in Computer Science 4693: Knowledge-Based Intelligent Information and Engineering Systems - KES 2007, pp. 509-516, Springer, Berlin, 2007.
(Japanese)