list of publications in english (excluding japanese publications) -...

Last Update: December 18, 2017

Google Scholar: 20,542 Citations in Total (h-index: 63) as of May 06, 2017. http://scholar.google.com/citations?user=vx9EZN4AAAAJ&hl=en

Scopus: 11,633 Citations in Total (h-index: 46) as of August 8, 2017.

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Web of Science: 6,528 Citations in Total (h-index: 38) as of August 8, 2017. http://apps.webofknowledge.com/CitationReport.do?product=UA&search_mode=CitationReport&SID=Z1t9y8xRes

A9P2mJKKk&page=1&cr_pqid=1&viewType=summary

List of Publications in English (Excluding Japanese Publications) Book [1] H. Ishibuchi, T. Nakashima, and M. Nii: Classification and Modeling with Linguistic Information Granules: Advanced

Approaches to Linguistic Data Mining, Springer, Berlin, November 2004. Edited Book [1] C. Grosan, A. Abraham, and H. Ishibuchi (eds.) Hybrid Evolutionary Algorithms, Springer, Berlin, August 14, 2007. [2] L. T. Bui, Y. S. Ong, N. X. Hoai, H. Ishibuchi, and P. N. Suganthan (eds.) Lecture Notes in Computer Science 7673:

Simulated Evolution and Learning - SEAL 2012, Springer, Berlin, December 2012. [3] H. Handa, H. Ishibuchi, Y. S. Ong, and K. C. Tan (eds.) Proceedings of the 18th Asia Pacific Symposium on Intelligent

and Evolutionary Systems, Volume 1 and Volume 2, Springer, Berlin, November 2014. [4] G. Dick, W. N. Browne, P. Whigham, M. Zhan, L. T. Bui, H. Ishibuchi, Y. Jin, X. Li, Y. Shi, P. Singh, K. C. Tan, and K.

Tang (eds.) Lecture Notes in Computer Science 8886: Simulated Evolution and Learning - SEAL 2014, Springer, Berlin, December 2014.

[5] H. Ishibuchi (ed.) Computational Intelligence (Volume 1), in Encyclopedia of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Paris, France, December 2015. [http://www.eolss.net].

[6] H. Ishibuchi (ed.) Computational Intelligence (Volume 2), in Encyclopedia of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Paris, France, December 2015. [http://www.eolss.net].

Book Chapters [1] H. Tanaka and H. Ishibuchi, “Possibilistic Regression Analysis based on Linear Programming.” J. Kacprzyk and M.

Fedrizzi (eds.) Fuzzy Regression Analysis, pp. 47-60, Omnitech Press, Warsaw, Poland, January 1992. [2] H. Tanaka, H. Ishibuchi, and T. Shigenaga, “Fuzzy Inference System based on Rough Sets and Its Application to Medical

Diagnosis.” R. Slowinski (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 111-117, Kluwer Academic Publishers, Dordrecht, Netherlands, June 1992.

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

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

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

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

http://scholar.google.com/citations?user=vx9EZN4AAAAJ&hl=en

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

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

[9] H. Ishibuchi, T. Murata, and T. Nakashima, “Genetic-Algorithm-Based Approaches to Classification Problems.” W. Pedrycz (ed.) Fuzzy Evolutionary Computation, pp. 127-154, Kluwer Academic Publishers, Boston, USA June 1997.

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

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

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

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

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

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

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

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

[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, Chapter 12, pp. 241-271, World Scientific Publishers, Singapore, April 2001.

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

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

[21] H. Ishibuchi and T. Yamamoto, “Comparison of Fuzzy Rule Selection Criteria for Classification Problems.” A. Abraham, J. Ruiz-del-Solar, and M. Koppen (eds.) Soft Computing Systems: Design, Management and Applications, pp. 132-141, IOS Press, Amsterdam, December 2002.

[22] H. Ishibuchi and T. Yoshida, “Hybrid Evolutionary Multi-Objective Optimization Algorithms.” A. Abraham, J. Ruiz-del-Solar, and M. Koppen (eds.) Soft Computing Systems: Design, Management and Applications, pp. 163-172, IOS Press, Amsterdam, December 2002.

[23] H. Ishibuchi and T. Yamamoto, “Trade-off between the Number of Fuzzy Rules and Their Classification Performance.” F. Herrera and L. Magdalena (eds.) Accuracy Improvements in Linguistic Fuzzy Modeling, pp. 72-99, Springer, Heidelberg, August 2003.

[24] H. Ishibuchi and S. Kaige, “A Simple but Powerful Multiobjective Hybrid Genetic Algorithms.” A. Abraham, M. Koppen, and K. Franke (eds.) Design and Application of Hybrid Intelligent Systems, pp. 244-251, IOS Press, Amsterdam, December 2003.

[25] T. Murata, S. Kaige, H. Ishibuchi, “Local Search Direction for Multi-Objective Optimization Using Memetic EMO

Algorithms.” Y. Jin (ed.) Knowledge Incorporation in Evolutionary Computation, Springer, pp. 385-410, Heidelberg, October 2004.

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

[27] H. Ishibuchi and Y. Nojima, “Fuzzy Ensemble Design through Multiobjective Fuzzy Rule Selection.” Y. Jin (ed.) Multi-Objective Machine Learning, Chapter 22, pp. 507-530, Springer, Berlin, May 2006.

[28] T. Nakashima and H. Ishibuchi, “Computational Intelligence in RoboCup Soccer Simulation.” G. Y. Yen and D. B. Fogel (eds.) Computational Intelligence: Principles and Practice, pp. 217-236, IEEE Computational Intelligence Society, July 2006.

[29] H. Ishibuchi, I. Kuwajima, and Y. Nojima, “Use of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection.” P. Melin, O. Castillo, E. G. Ramirez, J. Kacprzyk and W. Pedrycz (eds.) Analysis and Design of Intelligent Systems using Soft Computing Techniques (Advances in Soft Computing 41), pp. 387-396, Springer, Berlin June 2007.

[30] H. Ishibuchi and Y. Nojima, “Pattern Classification with Linguistic Rules H. Bustince.” F. Herrera, and J. Montero (eds.) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Intelligent Systems from Decision Making to Data Mining, Web Intelligence and Computer Vision, pp. 377-395, Springer, Berlin, November 2007.

[31] H. Ishibuchi, I. Kuwajima, and Y. Nojima, “Multiobjective Classification Rule Mining J. Knowles.” D. Corne, and K. Deb (eds.) Multi-Objective Problem Solving from Nature: From Concepts to Applications, pp. 219-240, Springer, Berlin, January 2008.

[32] H. Ishibuchi, I. Kuwajima, and Y. Nojima, “Evolutionary Multiobjective Rule Selection for Classification Rule Mining.” A. Ghosh, K. S. Dehuri, and S. Ghosh (eds.) Multi-objective Evolutionary Algorithms for Knowledge Discovery from Databases, Springer, Chapter 3, pp. 47-70, Berlin, March 2008.

[33] H. Ishibuchi, Y. Nojima, and I. Kuwajima, “Evolutionary Multiobjective Design of Fuzzy Rule-based Classifiers.” J. Fulcher and L. C. Jain (eds.) Computational Intelligence: A Compendium, Chapter 13, pp. 641-685, Springer, Berlin, July 2008.

[34] H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, “Implementation of Multiobjective Memetic Algorithms for Combinatorial Optimization Problems: A Knapsack Problem Case Study.” C. K. Goh, Y. S. Ong, and K. C. Tan (eds.) Multi-Objective Memetic Algorithms, pp. 27-49, Springer, Berlin, February 2009.

[35] H. Ishibuchi and Y. Nojima, “Multiobjective Genetic Fuzzy Systems.” C. L. Mumford and L. C. Jain (eds.) Computational Intelligence: Collaboration, Fusion and Emergence, pp. 131-173, Springer, Berlin, July 2009.

[36] A. Jaszkiewicz, H. Ishibuchi, and Q. Zhang, “Multiobjective Memetic Algorithms.” F. Neri, C. Cotta, and P. Moscato (eds.) Handbook of Memetic Algorithms, Chapter 13, pp. 201-217, Springer, Berlin, November 2011.

[37] Y. Nojima, S. Mihara, and H. Ishibuchi, “Parallel distributed genetic rule selection for data mining from large data sets.” F. Kojima, F. Kobayashi, and H. Nakamoto (eds.) Simulation and Modeling Related to Computational Science and Robotics Technology, pp. 140-154, IOS Press, Amsterdam (2012).

[38] H. Ishibuchi and Y. Nojima, “Multiobjective genetic fuzzy systems.” J. Kacprzyk and W. Pedrycz (eds.) Springer Handbook of Computational Intelligence, Springer-Verlag Berlin Heidelberg (2015).

Journal Papers [1] H. Ishibuchi, H. Tanaka, and 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, May 1990. [2] H. Ishibuchi and H. Tanaka, “Multiobjective programming in optimization of the interval objective function,” European

J. of Operational Research, vol. 48, no. 2, pp. 219-225, September 1990. [3] H. Tanaka and 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, May 1991. [4] H. Ishibuchi, R. Fujioka, and H. Tanaka, “Possibility and necessity pattern classification using neural networks,” Fuzzy

Sets and Systems, vol. 48, no. 3, pp. 331-340, June 1992. [5] H. Tanaka, H. Ishibuchi, and 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, August 1992. [6] H. Ishibuchi and H. Tanaka, “Fuzzy regression analysis using neural networks,” Fuzzy Sets and Systems, vol. 50, no. 3,

pp. 257-266, September 1992. [7] H. Ishibuchi, K. Nozaki, and H. Tanaka, “Distributed representation of fuzzy rules and its application to pattern

classification,” Fuzzy Sets and Systems, vol. 52, no. 1, pp. 21-32, November 1992. [8] H. Tanaka and H. Ishibuchi, “Evidence theory of exponential possibility distributions,” International J. of Approximate

Reasoning, vol. 8, no. 2, pp. 123-140, March 1993. [9] H. Ishibuchi, R. Fujioka, and H. Tanaka, “Neural networks that learn from fuzzy if-then rules,” IEEE Trans. on Fuzzy

Systems, vol. 1, no. 2, pp. 85-97, May 1993. [10] H. Ishibuchi, H. Tanaka, and 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, July 1993. [11] H. Tanaka, H. Ishibuchi, and I. Hayashi, “Identification method of possibility distributions and its application to

discriminant analysis,” Fuzzy Sets and Systems, vol. 58, no. 1, pp. 41-50, August 1993. [12] H. Ishibuchi, K. Nozaki, and H. Tanaka, “Efficient fuzzy partition of pattern space for classification problems,” Fuzzy

Sets and Systems, vol. 59, no. 3, pp. 295-304, November 1993. [13] H. Ishibuchi, H. Tanaka, and H. Okada, “Interpolation of fuzzy if-then rules by neural networks,” International J. of

Approximate Reasoning, vol. 10, no. 1, pp. 3-27, January 1994. [14] H. Ishibuchi, N. Yamamoto, S. Misaki, and 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, January 1994. [15] H. Ishibuchi, K. Nozaki, N. Yamamoto, and 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, February 1994.

[16] K. Kwon, H. Ishibuchi, and 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, April 1994.

[17] H. Ishibuchi, K. Nozaki, H. Tanaka, Y. Hosaka, and M. Matsuda, “Empirical study on learning in fuzzy systems by rice taste analysis,” Fuzzy Sets and Systems, vol. 64, no. 2, pp. 129-144, June 1994.

[18] 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, vol. 65, no. 2/3, pp. 237-253, August 1994.

[19] H. Ishibuchi, N. Yamamoto, T. Murata, and H. Tanaka, “Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems,” Fuzzy Sets and Systems, vol. 67, no. 1, pp. 81-100, October 1994.

[20] H. Tanaka, H. Ishibuchi, and S. Yoshikawa, “Exponential possibility regression analysis,” Fuzzy Sets and Systems, vol. 69, no. 3, pp. 305-318, February 1995.

[21] H. Ishibuchi, S. Misaki, and H. Tanaka, “Modified simulated annealing algorithms for the flow shop sequencing problem,” European J. of Operational Research, vol. 81, no. 2, pp. 388-398, March 1995.

[22] H. Ishibuchi, K. Kwon, and H. Tanaka, “A learning algorithm of fuzzy neural networks with triangular fuzzy weights,” Fuzzy Sets and Systems, vol. 71, no. 3, pp. 277-293, May 1995.

[23] 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, vol. 3, no. 3, pp. 260-270, August 1995.

[24] H. Ishibuchi, K. Morioka, and I. B. Turksen, “Learning by fuzzified neural networks,” International J. of Approximate Reasoning, vol. 13, no. 4, pp. 327-358, November 1995.

[25] K. Nozaki, H. Ishibuchi, and H. Tanaka, “Adaptive fuzzy rule-based classification systems,” IEEE Trans. on Fuzzy Systems, vol. 4, no. 3, pp. 238-250, August 1996.

[26] T. Murata, H. Ishibuchi, and H. Tanaka, “Multi-objective genetic algorithm and its application to flowshop scheduling,” Computer and Industrial Engineering, vol. 30, no. 4, pp. 957-968, October 1996.

[27] T. Murata, H. Ishibuchi, and H. Tanaka, “Genetic algorithms for flowshop scheduling problems,” Computer and Industrial Engineering, vol. 30, no. 4, pp. 1061-1071, October 1996.

[28] K. Nozaki, H. Ishibuchi, and 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, March 1997.

[29] H. Ishibuchi and 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, March 1997.

[30] H. Ishibuchi, T. Murata, and 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, July 1997.

[31] H. Ishibuchi, T. Nakashima, and 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, December 1997.

[32] H. Ishibuchi and 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, pp. 392-403, August 1998.

[33] T. Murata, M. Gen, and H. Ishibuchi, “Multi-objective scheduling with fuzzy due-date,” Computers and Industrial Engineering, vol. 35, no. 3-4, pp. 439-442, December 1998.

[34] H. Ishibuchi, T. Murata, and 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, December 1998.

[35] H. Ishibuchi, M. Nii, and 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, January 1999.

[36] H. Ishibuchi, C. H. Oh, and T.Nakashima, “Competition between strategies for a market selection game,” COMPLEXITY INTERNATIONAL (On-line Journal: http://www.csu.edu.au/ci/), vol. 6, January 1999.

[37] H. Ishibuchi, T. Nakashima, and T. Morisawa, “Voting in fuzzy rule-based systems for pattern classification problems,” Fuzzy Sets and Systems, vol. 103, no. 2, pp. 223-238, April 1999.

[38] H. Ishibuchi, T. Nakashima, and 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, pp. 601-618, October 1999.

[39] H. Ishibuchi, T. Murata, and 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, October 1999.

[40] H. Ishibuchi and T. Nakashima, “Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes,” IEEE Trans. on Industrial Electronics, vol. 46, no. 6, pp. 157-168, December 1999.

[41] H. Ishibuchi and T. Nakashima, “Pattern and feature selection by genetic algorithms in nearest neighbor classification,” Journal of Advanced Computational Intelligence, vol. 4, no. 2, pp. 138-145, April 2000.

[42] H. Ishibuchi and M. Nii, “Neural networks for soft decision making,” Fuzzy Sets and Systems, vol. 115, no. 1, pp. 121-140, October 2000.

[43] H. Ishibuchi and M. Nii, “Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks,” Fuzzy Sets and Systems, vol. 119, no. 2, pp. 273-290, April 2001.

[44] H. Ishibuchi and M. Nii, “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, June 2001.

[45] H. Ishibuchi, T. Nakashima, and T. Murata, “Three-objective genetics-based machine learning for linguistic rule

extraction,” Information Sciences, vol. 136, no. 1-4, pp. 109-133, August 2001. [46] H. Ishibuchi and T. Nakashima, “Effect of rule weights in fuzzy rule-based classification systems,” IEEE Trans. on

Fuzzy Systems, vol. 9, no. 4, pp. 506-515, August 2001. [47] H. Ishibuchi, R. Sakamoto, and T. Nakashima, “Evolution of unplanned coordination in a market selection game,” IEEE

Trans. on Evolutionary Computation, vol. 5, no. 5, pp. 524-534, October 2001. [48] H. Ishibuchi and 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, October 2002. [49] H. Ishibuchi, R. Sakamoto, and T. Nakashima, “Learning fuzzy rules from iterative execution of games,” Fuzzy Sets and

Systems, vol. 135, no. 2, pp. 213-240, April 2003. [50] H. Ishibuchi, T. Yoshida, and T. Murata, “Balance between genetic search and local search in memetic algorithms for

multiobjective permutation flowshop scheduling,” IEEE Trans. on Evolutionary Computation, vol. 7, no. 2, pp. 204-223, April 2003.

[51] H. Ishibuchi and 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, January 2004.

[52] H. Ishibuchi and 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, January 2004.

[53] H. Ishibuchi and 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, June 2004.

[54] H. Ishibuchi, T. Yamamoto, and 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, April 2005.

[55] H. Ishibuchi and T. Yamamoto, “Rule weight specification in fuzzy rule-based classification systems,” IEEE Trans. on Fuzzy Systems, vol. 13, no. 4, pp. 428-435, August 2005.

[56] H. Ishibuchi and 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, December 2005.

[57] H. Ishibuchi, K. Narukawa, and Y. Nojima, “Handling of overlapping objective vectors in evolutionary multiobjective optimization,” International Journal of Computational Intelligence Research, vol. 1, no. 1, pp. 1-18, December 2005.

[58] H. Ishibuchi, T. Yamamoto, and T. Nakashima, “An approach to fuzzy default reasoning for function approximation,” Soft Computing, vol. 10, no. 9, pp. 850-864, July 2006.

[59] 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.

[60] 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, January 2007.

[61] 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, February 2007.

[62] 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, July 2007.

[63] 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, July 2007.

[64] 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, October 2007.

[65] T. Nakashima, Y. Yokota, H. Ishibuchi, and G. Schaefer, “A cost-based fuzzy system for pattern classification with class importance,” International Journal of Artificial Life and Robotics, vol. 12, no. 1-2, pp. 43-46, April 2008.

[66] H. Ishibuchi, K. Narukawa, N. Tsukamoto, and Y. Nojima, “An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization,” European Journal of Operational Research, vol. 188, no. 1, pp. 57-75, July 2008.

[67] I. Kuwajima, Y. Nojima, and H. Ishibuchi, “Effects of constructing fuzzy discretization from crisp discretization for rule-based classifiers,” Artificial Life and Robotics, vol. 13, no. 1, pp. 294-297, December 2008

[68] I. Kuwajima, Y. Nojima, and H. Ishibuchi, “Obtaining accurate classifiers with Pareto-optimal and near Pareto-optimal rules,” Artificial Life and Robotics, vol. 13, no. 1, pp. 315-319, December 2008.

[69] N. Tsukamoto, Y. Nojima, and H. Ishibuchi, “Effects of non-geometric binary crossover on multiobjective 0/1 knapsack problems,” Artificial Life and Robotics, vol. 13, no. 2, pp. 434-437, February 2009.

[70] Y. Nojima, H. Ishibuchi, and I. Kuwajima, “Parallel distributed genetic fuzzy rule selection,” Soft Computing, vol. 13, no. 5, pp. 511-519, March 2009.

[71] H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, “Use of biased neighborhood structures in multiobjective memetic algorithms,” Soft Computing, vol. 13, no. 8-9, pp. 795-810, July 2009.

[72] Y. Hamada, Y. Nojima, and H. Ishibuchi, “Use of multi-objective genetic rule selection for examining the effectiveness of inter-vehicle communication in traffic simulations,” Artificial Life and Robotics, vol. 14, no. 3, pp. 410-413, December 2009.

[73] H. Ohyanagi, Y. Wakamatsu, Y. Nakashima, Y. Nojima, and H. Ishibuchi, “Evolution of cooperative behavior among heterogeneous agents with different strategy representations in an iterated prisoner’s dilemma,” Artificial Life and Robotics, vol. 14, no. 3, pp. 414-417, December 2009.

[74] Y. Nojima and H. Ishibuchi, “Incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems,” Artificial Life and Robotics, vol. 14, no. 3, pp. 418-421, December 2009.

[75] Y. Nojima, Y. Hamada, and H. Ishibuchi, “Application of interactive fuzzy data mining to the analysis of inter-vehicle communication in traffic simulations,” ICGST International Journal on Automation, Robotics and Autonomous Systems, vol. 9, no. 2, pp. 17-25, December 2009.

[76] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Diversity improvement by non-geometric binary crossover in evolutionary multiobjective optimization,” IEEE Trans. on Evolutionary Computation, vol. 14., no. 6, pp. 985-998, December 2010.

[77] H. Ishibuchi, Y. Kaisho, and Y. Nojima, “Design of linguistically interpretable fuzzy rule-based classifiers: A short review and open questions,” Journal of Multiple-Valued Logic and Soft Computing, vol. 17, no. 2-3, pp. 101-134, March 2011.

[78] H. Ishibuchi, H. Ohyanagi, and Y. Nojima, “Evolution of strategies with different representation schemes in a spatial iterated prisoner’s dilemma game,” IEEE Trans. on Computational Intelligence and AI in Games, vol. 3, no. 1, pp. 67-82, March 2011.

[79] H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, “Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization,” Soft Computing, vol. 15, no. 9, pp. 1749-1767, September 2011.

[80] H. Ishibuchi, Y. Nakashima, and Y. Nojima, “Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning,” Soft Computing, vol. 15, no. 12, pp. 2415-2434, November 2011.

[81] R. Alcalá, Y. Nojima, F. Herrera, and H. Ishibuchi, “Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions,” Soft Computing, vol. 15, no. 12, pp. 2303-2318, November 2011.

[82] M. Fazzolari, R. Alcalá, Y. Nojima, H. Ishibuchi, and F. Herrera, “A review of the application of multiobjective evolutionary fuzzy systems: Current status and further directions,” IEEE Trans. on Fuzzy Systems, vol. 21, no. 1, pp. 45-65, February 2013.

[83] H. Ishibuchi, S. Mihara, and Y. Nojima, “Parallel distributed hybrid fuzzy GBML models with rule set migration and training data rotation,” IEEE Trans. on Fuzzy Systems, vol. 21, no. 2, pp. 355-368, April 2013.

[84] Z. Deng, Y. Jian, F.-L. Chung, H. Ishibuchi, and S. Wang, “Knowledge-leverage-based fuzzy system and its modeling,” IEEE Trans. on Fuzzy Systems, vol. 21, no. 4, pp. 597-609, August 2013.

[85] H. Ishibuchi and Y. Nojima, “Repeated double cross-validation for choosing a single solution in evolutionary multi-objective fuzzy classifier design,” Knowledge-Based Systems, vol. 54, pp. 22-31, December 2013.

[86] M. Xu, H. Ishibuchi, X. Gu, S. Wang, “Dm-KDE: Dynamical kernel density estimation by sequences of KDE estimators with fixed number of components over data streams,” Frontiers of Computer Science, vol. 8, no. 4, pp. 563-580, August 2014.

[87] C. H. Tan, K. S. Yap, H. Ishibuchi, Y. Nojima, and H. J. Yap, “Application of fuzzy inference rules to early semi-automatic estimation of activity duration in software project management,” IEEE Trans. on Human-Machine Systems, vol. 44, no. 5, pp. 678-688, October 2014.

[88] H. T. T. Binh, B. T. Lam, N. S. T. Ha, and H. Ishibuchi, “A multi-objective approach for solving the survivable network design problem with simultaneous unicast and anycast Flows,” Applied Soft Computing, vol. 24, pp. 1145–1154, November 2014.

[90] Y. Jiang, F.-L. Chung, H. Ishibuchi, Z. Deng, and S. Wang, “Multitask TSK fuzzy system modeling by mining intertask common hidden structure,” IEEE Trans. on Cybernetics, vol. 45, no. 3, pp. 548-561, March 2015.

[91] H. Ishibuchi, N. Akedo, and Y. Nojima, “Behavior of multi-objective evolutionary algorithms on many-objective knapsack problems,” IEEE Trans. on Evolutionary Computation, vol. 19, no. 2, pp. 264-283, April 2015.

[92] X. Gu, F.-L. Chung, H. Ishibuchi, S. Wang, “Multitask coupled logistic regression and its fast implementation for large multitask datasets,” IEEE Trans. on Cybernetics, vol. 45, no. 9, pp. 1953-1966, September 2015.

[93] H. Ishibuchi, T. Sudo, and Y. Nojima, “Interactive evolutionary computation with minimum fitness evaluation requirement and offline algorithm design,” SpringerPlus, vol. 5 (doi:10.1186/s40064-016-1789-1) 2016.

[94] K. Narukawa,Y. Setoguchi, Y. Tanigaki, M. Olhofer, B. Sendhoff, and H. Ishibuchi, “Preference representation using Gaussian functions on a hyperplane in evolutionary multi-objective optimization,” Soft Computing, vol. 20, no. 7, pp. 2733-2757, July 2016.

[95] H. Ishibuchi, H. Masuda, and Y. Nojima, “Pareto fronts of many-objective degenerate test problems,” IEEE Trans. on Evolutionary Computation, vol. 20, no. 5, pp. 807-813, October 2016.

[96] Z. Deng, Y. Jiang, F.-L. Chung, H. Ishibuchi, K.-S. Choi, and S. Wang, “Transfer prototype-based fuzzy clustering,” IEEE Trans. on Fuzzy Systems, vol. 24, no. 5, pp. 1210-1232, October 2016.

[97] Z. Deng, Y. Jiang, H. Ishibuchi, K.-S. Choi, and S. Wang, “Enhanced knowledge-leverage-based TSK fuzzy system modeling for inductive transfer learning,” ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 1, Article No. 11, October 2016 (Online Journal).

[98] H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, “Performance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes,” IEEE Trans. on Evolutionary Computation, vol. 21, no.2, pp. 169-190, April 2017.

[99] Z. Wang, Q. Zhang, H. Li, H. Ishibuchi, and L. Jiao, “On the use of two reference points in decomposition based multiobjective evolutionary algorithms,” Swarm and Evolutionary Computation, vol. 34, pp. 89-102, June 2017.

[100] X. Gu, F.-L. Chung, H. Ishibuchi and S. Wang, “Imbalanced TSK fuzzy classifier by cross-class Bayesian fuzzy clustering and imbalance learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 2005-2020, August 2017.

[101] R. Wang, J. Xiong, H. Ishibuchi, G. Wu, and T. Zhang, “On the effect of reference point in MOEA/D for multi-objective optimization,” Applied Soft Computing, vol. 58, pp. 25-34, September 2017.

[102] R. Tanabe, H. Ishibuchi, and A. Oyama, “Benchmarking multi- and many-objective evolutionary algorithms under two optimization scenarios,” IEEE Access, vol. 5, pp. 19597-19619, Dec 2017.

[103] H. Ishibuchi, K. Doi, and Y. Nojima, “On the effect of normalization in MOEA/D for multi-objective and many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 4, pp. 279–294, Dec 2017.

[104] T. Zhou, H. Ishibuchi, and S. Wang, "Stacked-structure-based hierarchical Takagi-Sugeno-Kang fuzzy classification through feature augmentation," IEEE Trans. on Emerging Topics in Computational Intelligence, vol. 1, no. 6, pp. 421-436, December 2017.

[105] R. Wang, Z. Zhou, H. Ishibuchi, T. Liao, and T. Zhang, “Localized weighted sum method for many-objective optimization,” IEEE Trans. on Evolutionary Computation (Accepted: Online Available as an Early Access paper).

[106] H. Zille, H. Ishibuchi, S. Mostaghim and Y. Nojima, “A framework for large-scale multi-objective optimization based on problem transformation,” IEEE Trans. on Evolutionary Computation (Accepted).

[107] Y. Zhang, H. Ishibuchi and S. Wang, “Deep Takagi-Sugeno-Kang fuzzy classifier with shared linguistic fuzzy rules,” IEEE Trans. on Fuzzy Systems (Accepted)

[108] M. Chica, R. Chiong, M. Kirley, and H. Ishibuchi, “A networked N-player trust game and its evolutionary dynamics,” IEEE Trans. on Evolutionary Computation (Accepted).

[109] H. Ishibuchi, R. Imada, Y. Setoguchi, and Yusuke Nojima, “Reference point specification in inverted generational distance for triangular linear Pareto front,” IEEE Trans. on Evolutionary Computation (Accepted).

International Conference Presentations [1] H. Ishibuchi and H. Tanaka, “The analysis of interval 0-1 problem by multi-objective programming,” Proc. of

International Workshop on Fuzzy System Applications, pp. 159-160, Iizuka, Japan, August 1988. [2] H. Tanaka and H. Ishibuchi, “Identification of possibilistic linear systems by quadratic membership functions of fuzzy

parameters,” Proc. of 3rd IFSA Congress, pp. 516-519, Seattle, USA, August 1989. [3] H. Ishibuchi, H. Tanaka, and N. Iwamoto, “Discriminant analysis of multi-dimensional interval data and its application to

smell sensing,” Proc. of 3rd IFSA Congress, pp. 532-535, Seattle, USA, August 1989. [4] H. Ishibuchi and H. Tanaka, “Identification of real-valued and interval-valued membership functions by neural

networks,” Proc. of International Conference on Fuzzy Logic & Neural Networks, pp. 179-182, Iizuka, Japan, July 1990. [5] H. Ishibuchi and H. Tanaka, “Several formulations of interval regression analysis,” Proc. of Sino-Japan Joint Meeting on

Fuzzy Sets and Systems, Section B2-2, Beijing, China, October 1990. [6] H. Tanaka and H. Ishibuchi, “Reduction of information systems based on rough sets and its application to fuzzy expert

systems,” Proc. of Sino-Japan Joint Meeting on Fuzzy Sets and Systems, Section C2-5, Beijing, China, October 1990. [7] R. Fujioka, H. Ishibuchi, H. Tanaka, and M. Omae, “Learning algorithm of neural networks for interval-valued data,”

Proc. of 4th IFSA Congress, Part of Artificial Intelligence, pp. 37-40, Brussels, Belgium, July 1991. [8] H. Ishibuchi, R. Fujioka, and H. Tanaka, “Possibility and necessity data analysis using neural networks,” Proc. of 4th

IFSA Congress, Part of Artificial Intelligence, pp. 74-77, Brussels, Belgium, July 1991. [9] H. Tanaka, H. Ishibuchi, and T. Shigenaga, “Fuzzy expert systems based on rough sets,” Proc. of 4th IFSA Congress,

Part of Artificial Intelligence, pp. 192-195, Brussels, Belgium, July 1991. [10] H. Tanaka, H. Ishibuchi, and I. Hayashi, “Identification method of possibility distributions and its application to pattern

recognition,” Proc. of 4th IFSA Congress, Part of Computer, Management and Systems Science, pp. 272-275, Brussels, Belgium, July 1991.

[11] H. Ishibuchi and H. Tanaka, “Comparison of fuzzy regression methods on working time analysis,” Abstract Booklet of

11th European Congress on Operational Research, p. 120, Aachen, Germany, July 1991. [12] H. Ishibuchi, R. Tamura, and H. Tanaka, “Flow shop scheduling by simulated annealing,” Abstract Booklet of 11th

European Congress on Operational Research, pp. 120-121, Aachen, Germany, July 1991. [13] H. Tanaka, H. Ishibuchi, and S. G. Hwang, “Fuzzy regression analysis with similarity relations,” Abstract Booklet of 11th

European Congress on Operational Research, p. 237, Aachen, Germany, July 1991. [14] H. Tanaka and H. Ishibuchi, “Normal possibility distribution and its application,” Proc. of International Fuzzy

Engineering Symposium, pp. 51-59, Yokohama, Japan, November 1991. [15] H. Ishibuchi and H. Tanaka, “Determination of fuzzy regression models by neural networks,” Proc. of International

Fuzzy Engineering Symposium, pp. 523-534, Yokohama, Japan, November 1991. [16] H. Ishibuchi and H. Tanaka, “An extension of the BP-algorithm to interval input vectors,” Proc. of IEEE International

Joint Conference on Neural Networks, pp. 1588-1593, Singapore, November 1991. [17] H. Ishibuchi and H. Tanaka, “Regression analysis with interval model by neural networks,” Proc. of IEEE International

Joint Conference on Neural Networks, pp. 1594-1599, Singapore, November 1991. [18] H. Ishibuchi, S. Misaki, and H. Tanaka, “Parallel algorithm of simulated annealing with modified generation

mechanism,” Proc. of IEEE International Joint Conference on Neural Networks, pp. 2434-2439, Singapore, November 1991.

[19] H. Tanaka and H. Ishibuchi, “Evidence theory of normal possibility and its application,” Proc. of IEEE International Conference on Fuzzy Systems, pp. 55-62, San Diego, USA, March 1992.

[20] H. Ishibuchi, K. Nozaki, and H. Tanaka, “Pattern classification by distributed representation of fuzzy rules,” Proc. of IEEE International Conference on Fuzzy Systems, pp. 643-650, San Diego, USA, March 1992.

[21] H. Ishibuchi, R. Fujioka, and H. Tanaka, “An architecture of neural network for input vectors of fuzzy numbers,” Proc. of IEEE International Conference on Fuzzy Systems, pp. 1293-1300, San Diego, USA, March 1992.

[22] H. Ishibuchi, H. Okada, and H. Tanaka, “Interpolation of fuzzy if-then rules by neural networks,” Proc. of 2nd International Conference on Fuzzy Logic & Neural Networks, pp. 337-340, Iizuka, Japan, July 1992.

[23] H. Ishibuchi, K. Nozaki, and H. Tanaka, “Efficient fuzzy partition of pattern space for classification problems,” Proc. of 2nd International Conference on Fuzzy Logic & Neural Networks, pp. 671-674, Iizuka, Japan, July 1992.

[24] H. Ishibuchi, S. Misaki, and H. Tanaka, “Simulated annealing with modified generation mechanism for flow shop scheduling problem,” Proc. of International Symposium on Robotics, Mechatronics and Manufacturing Systems, pp. 21-26, Kobe, Japan, September 1992.

[25] S. Misaki, H. Ishibuchi, and H. Tanaka, “Fuzzy flow shop scheduling by simulated annealing,” Proc. of Pacific Conference on Manufacturing, pp. 531-538, Sakai, Japan, November 1992.

[26] H. Ishibuchi, H. Okada, and H. Tanaka, “Learning of neural networks from fuzzy inputs and fuzzy targets,” Proc. of IEEE International Joint Conference on Neural Networks , vol. 3, pp. 447-452, Beijing, China, November 1992.

[27] H. Ishibuchi, H. Okada, and H. Tanaka, “A neural network with interval weights and its learning algorithm,” Proc. of IEEE International Joint Conference on Neural Networks, vol. 3, pp. 507-511 & p. 481, Beijing, China, November 1992.

[28] H. Ishibuchi, H. Tanaka, and H. Okada, “Fuzzy neural networks with fuzzy weights and fuzzy biases,” Proc. of IEEE

International Conferences on Neural Networks, pp. 1650-1655, San Francisco, USA, March 1993.

[29] H. Ishibuchi, K. Nozaki, H. Tanaka, Y. Hosaka, and M. Matsuda, “Empirical study on learning in fuzzy systems,” Proc.

of 2nd IEEE International Conference on Fuzzy Systems, pp. 606-611, San Francisco, USA, March 1993.

[30] H. Ishibuchi, K. Nozaki, and N. Yamamoto, “Selecting fuzzy rules by genetic algorithm for classification problems,”

Proc. of 2nd IEEE International Conference on Fuzzy Systems, pp. 1119-1124, San Francisco, USA, March, 1993.

[31] H. Tanaka and H. Ishibuchi, “Possibility analysis by exponential possibility distributions,” Proc. of 2nd IEEE

International Conference on Fuzzy Systems, pp. 1208-1213, San Francisco, USA, March 1993.

[32] H. Ishibuchi, K. Nozaki, and N. Yamamoto, “Genetic operations for rule selection in fuzzy classification systems,” Proc.

of 5th IFSA World Congress, pp. 15-18, Seoul, Korea, July 1993.

[33] H. Ishibuchi, K. Kwon, and H. Tanaka, “Learning of fuzzy neural networks from fuzzy inputs and fuzzy targets,” Proc.

of 5th IFSA World Congress, pp. 147-150, Seoul, Korea, July 1993.

[34] K. Nozaki, H. Ishibuchi, and H. Tanaka, “Performance evaluation of fuzzy-rule-based classification methods,” Proc. of

5th IFSA World Congress, pp. 167-170, Seoul, Korea, July 1993.

[35] H. Ishibuchi and H. Tanaka, “A unified approach to possibility and necessity regression analysis with interval regression

models,” Proc. of 5th IFSA World Congress, pp. 501-504, Seoul, Korea, July 1993.

[36] N. Yamamoto, H. Ishibuchi, and H. Tanaka, “Fuzzy flow shop scheduling by GA, SA and Taboo Search,” Proc. of 5th

IFSA World Congress, pp. 576-579, Seoul, Korea, July 1993.

[37] H. Tanaka, K. Yokode, and H. Ishibuchi, “GMDH by fuzzy if-then rules with certainty factors,” Proc. of 5th IFSA World

Congress, pp. 802-805, Seoul, Korea, July 1993.

[38] H. Ishibuchi, K. Kwon, and H. Tanaka, “Implementation of fuzzy if-then rules by fuzzy neural networks with fuzzy

weights,” Proc. of 1st European Congress on Fuzzy and Intelligent Technologies, pp. 209-215, Aachen, Germany,

September 1993.

[39] H. Ishibuchi, K. Nozaki, and R. Weber, “Approximate pattern classification with fuzzy boundary,” Proc. of International

Joint Conference on Neural Networks, pp. 693-696, Nagoya, Japan, October 1993.

[40] K. Kwon, H. Ishibuchi, and H. Tanaka, “Nonlinear mapping of interval vectors by neural networks,” Proc. of

International Joint Conference on Neural Networks, pp. 758-761, Nagoya, Japan, October 1993.

[41] H. Ishibuchi, A. Miyazaki, K. Kwon, and H. Tanaka, “Learning from incomplete training data with missing values and

medical application,” Proc. of International Joint Conference on Neural Networks, pp. 1871-1874, Nagoya, Japan,

October 1993.

[42] H. Ishibuchi and A. Miyazaki, “Determination of inspection order for classifying new samples by neural networks,” Proc.

of IEEE International Conference on Neural Networks, pp. 2907-2910, Orlando, USA, June 1994.

[43] H. Ishibuchi, A. Miyazaki, and H. Tanaka, “Neural-network-based diagnosis systems for incomplete data with missing

inputs,” Proc. of IEEE International Conference on Neural Networks, pp. 3457-3460, Orlando, USA, June 1994.

[44] A. Miyazaki, K. Kwon, H. Ishibuchi, and H. Tanaka, “Fuzzy regression analysis by fuzzy neural networks and its

application,” Proc. of 3rd IEEE International Conference on Fuzzy Systems, pp. 52-57, Orlando, USA, June 1994.

[45] H. Ishibuchi, K. Morioka, and H. Tanaka, “A fuzzy neural network with trapezoid fuzzy weights,” Proc. of 3rd IEEE

International Conference on Fuzzy Systems, pp. 228-233, Orlando, USA, June 1994.

[46] K. Nozaki, H. Ishibuchi, and H. Tanaka, “Trainable fuzzy classification systems based on fuzzy if-then rules,” Proc. of

3rd IEEE International Conference on Fuzzy Systems, pp. 498-502, Orlando, USA, June 1994.

[47] K. Nozaki, H. Ishibuchi, and H. Tanaka, “Selecting fuzzy rules with forgetting in fuzzy classification systems,” Proc. of

3rd IEEE International Conference on Fuzzy Systems, pp. 618-623, Orlando, USA, June 1994.

[48] H. Tanaka, S. Yoshikawa, and H. Ishibuchi, “Discriminant analysis based on exponential possibility distributions,” Proc.

of 3rd IEEE International Conference on Fuzzy Systems, pp. 802-807, Orlando, USA, June 1994.

[49] H. Ishibuchi, K. Nozaki, N. Yamamoto, and H .Tanaka, “Acquisition of fuzzy classification knowledge using genetic

algorithms,” Proc. of 3rd IEEE International Conference on Fuzzy Systems, pp. 1963-1968, Orlando, USA, June 1994. [50] T. Murata and H. Ishibuchi, “Performance evaluation of genetic algorithms for flowshop scheduling problems,” Proc. of

1st IEEE Conference on Evolutionary Computation, pp. 812-817, Orlando, USA, June 1994. [51] T. Murata, H. Ishibuchi, and H. Tanaka, “Crossover and mutation operators in genetic algorithms for flowshop

scheduling problems,” Abstracts of 3rd Conference of the Association of Asian-Pacific Operational Research Societies with IFORS, p. 140, Fukuoka, Japan, July 1994.

[52] H. Ishibuchi and H. Tanaka, “Interval programming problems based on order relations and inequality relations,” Abstracts of 3rd Conference of the Association of Asian-Pacific Operational Research Societies with IFORS, p. 297, Fukuoka, Japan, July 1994.

[53] H. Ishibuchi, “A general approach to the learning of fuzzy connection weights,” Proc. of 3rd International Conference on Fuzzy Logic, Neural Nets and Soft Computing, pp. 163-164, Iizuka, Japan, August 1994.

[54] T. Murata and H. Ishibuchi, “Adjusting membership functions of fuzzy classification rules by genetic algorithms,” Proc. of International Joint Conference of 4th IEEE International Conference on Fuzzy Systems and 2nd International Fuzzy Engineering Symposium, pp. 1819-1824, Yokohama, Japan, March 1995.

[55] H. Ishibuchi and K. Morioka, “Determination of type II membership functions by fuzzified neural networks,” Proc. of 3rd European Congress on Intelligent Techniques and Soft Computing, pp. 529-533, Aachen, Germany, August 1995.

[56] K. Nozaki, T. Morisawa, and H. Ishibuchi, “Adjusting membership functions in fuzzy-rule-based classification systems,” Proc. of 3rd European Congress on Intelligent Techniques and Soft Computing, pp. 615-619, Aachen, Germany, August 1995.

[57] H. Ishibuchi, T. Murata, and I. B. Turksen, “A genetic-algorithm-based approach to the selection of linguistic classification rules,” Proc. of 3rd European Congress on Intelligent Techniques and Soft Computing, pp. 1415-1419, Aachen, Germany, August 1995.

[58] H. Ishibuchi, T. Murata, and I. B. Turksen, “Selecting linguistic classification rules by two-objective genetic algorithms,” Proc. of 1995 IEEE International Conference on Systems, Man and Cybernetics, pp. 1410-1415, Vancouver, Canada, October 1995.

[59] H. Ishibuchi, T. Nakashima, and T. Murata, “A fuzzy classifier system for generating linguistic classification rules,” Proc. of 1995 IEEE/Nagoya University World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Evolutionary Computation, pp. 22-27, Nagoya, Japan, November 1995.

[60] T. Murata and H. Ishibuchi, “MOGA: Multi-objective genetic algorithms,” Proc. of 1995 IEEE International Conference on Evolutionary Computation, pp. 289-294, Perth, Australia, November 1995.

[61] H. Ishibuchi, T. Nakashima, and T. Murata, “A fuzzy classifier system that generates fuzzy if-then rules for pattern classification problems,” Proc. of 1995 IEEE International Conference on Evolutionary Computation, pp. 759-764, Perth, Australia, November 1995.

[62] H. Ishibuchi and K. Morioka, “Classification of fuzzy input patterns by neural networks,” Proc. of 1995 IEEE International Conference on Neural Networks, pp. 3118-3123, Perth, Australia, November 1995.

[63] H. Ishibuchi and T. Murata, “Multi-objective genetic local search algorithm,” Proc. of 1996 IEEE International Conference on Evolutionary Computation, pp. 119-124, Nagoya, Japan, May 1996.

[64] T. Murata and H. Ishibuchi, “Positive and negative combination effects of crossover and mutation operators in sequencing problems,” Proc. of 1996 IEEE International Conference on Evolutionary Computation, pp. 170-175, Nagoya, Japan, May 1996.

[65] H. Ishibuchi, T. Nakashima, and T. Murata, “Genetic-algorithm-based approaches to the design of fuzzy systems for multi-dimensional pattern classification problems,” Proc. of 1996 IEEE International Conference on Evolutionary Computation, pp. 229-234, Nagoya, Japan, May 1996.

[66] H. Ishibuchi and M. Nii, “Generating fuzzy if-then rules from trained neural networks: Linguistic analysis of neural networks,” Proc. of 1996 IEEE International Conference on Neural Networks, pp. 1133-1138, Washington DC, USA, June 1996.

[67] H. Ishibuchi and M. Nii, “Fuzzy regression analysis by neural networks with non-symmetric fuzzy number weights,” Proc. of 1996 IEEE International Conference on Neural Networks, pp. 1191-1196, Washington DC, USA, June 1996.

[68] T. Murata, H. Ishibuchi, and K. H. Lee, “Application of two-objective genetic algorithm to flowshop scheduling problems with interval processing time,” Proc. of 4th European Congress on Intelligent Techniques and Soft Computing, pp. 443-447, Aachen, Germany, September 1996.

[69] H. Ishibuchi, T. Morisawa, and T. Nakashima, “Two fuzzy reasoning methods in fuzzy-rule-based classification systems,” Proc. of 4th European Congress on Intelligent Techniques and Soft Computing, pp. 685-689, Aachen, Germany, September 1996.

[70] H. Ishibuchi, T. Murata, and K. H. Lee, “Formulation of fuzzy flowshop scheduling problems with fuzzy processing time,” Proc. of 1996 IEEE International Conference on Fuzzy Systems, pp. 199-205, New Orleans, USA, September 1996.

[71] H. Ishibuchi and M. Nii, “Fuzzy regression analysis with non-symmetric fuzzy number coefficients and its neural network implementation,” Proc. of 1996 IEEE International Conference on Fuzzy Systems, pp. 318-324, New Orleans, USA, September 1996.

[72] H. Ishibuchi and M. Nii, “Learning of fuzzy connection weights in fuzzified neural networks,” Proc. of 1996 IEEE International Conference on Fuzzy Systems, pp. 373-379, New Orleans, USA, September 1996.

[73] H. Ishibuchi, T. Morisawa, and T. Nakashima, “Voting schemes for fuzzy-rule-based classification systems,” Proc. of 1996 IEEE International Conference on Fuzzy Systems, pp. 614-620, New Orleans, USA, September 1996.

[74] H. Ishibuchi, T. Morisawa, and T. Nakashima, “Combining multiple fuzzy-rule-based classification systems,” Proc. of 4th International Conference on Soft Computing, pp. 822-825, Iizuka, Japan, October 1996.

[75] T. Murata and H. Ishibuchi, “Multi-objective genetic algorithm for flowshop scheduling,” Proc. of Pacific Conference on Manufacturing, pp. 353-358, Seoul, Korea, October 1996.

[76] H. Ishibuchi, T. Murata, and K. H. Lee, “Flowshop scheduling with fuzzy processing time,” Proc. of Pacific Conference on Manufacturing, pp. 359-364, Seoul, Korea, October 1996.

[77] H. Ishibuchi and T. Nakashima, “A coding method for generating disjunctive fuzzy if-then rules,” Proc. of 1st Asia-Pacific Conference on Simulated Evolution and Learning, pp. 83-90, Taejon, Korea, November 1996.

[78] H. Ishibuchi, T. Murata, and K. H. Lee, “Relations between conventional scheduling problems and fuzzy scheduling problems,” Proc. of 35th IEEE Conference on Decision and Control, pp. 106-107, Kobe, Japan, December 1996.

[79] H. Ishibuchi, T. Nakashima, and T. Murata, “Several variants of fuzzy classifier systems for pattern classification problems with continuous attributes,” Proc. of International Symposium on Artificial Life and Robotics, pp. 46-49, Beppu, Japan, February 1997.

[80] H. Ishibuchi, C. H. Oh, and T. Nakashima, “Improving the performance of Q-learning by fuzzy logic,” Proc. of International Symposium on Artificial Life and Robotics, pp. 50-53, Beppu, Japan, February 1997.

[81] H. Ishibuchi and T. Nakashima, “Evolution of fuzzy nearest neighbor neural networks,” Proc. of 1997 IEEE International Conference on Evolutionary Computation, pp. 673-678, Indianapolis, USA, April 1997.

[82] H. Ishibuchi and M. Nii, “Possibilistic fuzzy classification using neural networks,” Proc. of 1997 IEEE International Conference on Neural Networks, pp. 1433-1438, Houston, USA, June 1997.

[83] H. Ishibuchi, M. Nii, and T. Murata, “Linguistic rule extraction from neural networks and genetic-algorithm-based rule selection,” Proc. of 1997 IEEE International Conference on Neural Networks, pp. 2390-2395, Houston, USA, June 1997.

[84] H. Ishibuchi and T. Murata, “Minimizing the fuzzy rule base and maximizing its performance by a multi-objective genetic algorithm, Proc. of 1997 IEEE International Conference on Fuzzy Systems, pp. 259-264, Barcelona, Spain, July 1997.

[85] T. Murata, H. Ishibuchi, and K. H. Lee, “Reformulation of various non-fuzzy scheduling problems using the concept of fuzzy due-date,” Proc. of 1997 IEEE International Conference on Fuzzy Systems, pp. 447-452, Barcelona, Spain, July

1997. [86] T. Nakashima, T. Morisawa, and H. Ishibuchi, “Input selection in fuzzy rule-based classification systems,” Proc. of 1997

IEEE International Conference on Fuzzy Systems, pp. 1457-1462, Barcelona, Spain, July 1997. [87] H. Ishibuchi, T. Nakashima, H. Miyamoto, and C. H. Oh, “Fuzzy Q-learning for a multi-player non-cooperative repeated

game,” Proc. of 1997 IEEE International Conference on Fuzzy Systems, pp. 1573-1579, Barcelona, Spain, July 1997. [88] H. Ishibuchi, T. Murata, and S. Tomioka, “Effectiveness of genetic local search algorithms,” Proc. of 7th International

Conference on Genetic Algorithms, pp. 505-512, East Lansing, USA, July 1997. [89] H. Ishibuchi, T. Murata, and S. Tomioka, “Performance of multi-objective genetic algorithms for flowshop scheduling

problems,” Proc. of 14th International Conference on Production Research, pp. 498-501, Osaka, Japan, August 1997. [90] H. Ishibuchi, T. Murata, and S. Tomioka, “Performance evaluation of genetic local search algorithms on flowshop

scheduling problems,” Proc. of 14th International Conference on Production Research, pp. 502-505, Osaka, Japan, August 1997.

[91] H. Ishibuchi and T. Nakashima, “Performance evaluation of various variants of fuzzy classifier systems for pattern classification problems,” Proc. of North American Fuzzy Information Processing Society Meeting, pp. 245-250, Syracuse, USA, September 1997.

[92] H. Ishibuchi, T. Nakashima, and T. Morisawa, “Simple fuzzy rule-based classification systems perform well on commonly used real world data sets,” Proc. of North American Fuzzy Information Processing Society Meeting, pp. 251-256, Syracuse, USA, September 1997.

[93] H. Ishibuchi and M. Nii, “Learning of neural networks from linguistic knowledge and numerical data,” Proc. of 1997 IEEE International Conference on Systems, Man and Cybernetics, pp. 1445-1450, Orlando, USA, October, 1997.

[94] T. Murata, M. Gen, and H. Ishibuchi, “Multi-objective scheduling with fuzzy due-date,” Proc. of 22nd International Conference on Computers and Industrial Engineering, pp. 176-179, Cairo, Egypt, December 1997.

[95] H. Ishibuchi, T. Murata, and M. Gen, “Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms,” Proc. of 22nd International Conference on Computers and Industrial Engineering, pp. 316-319, Cairo, Egypt, December 1997.

[96] T. Murata, H. Ishibuchi, T. Nakashima, and M. Gen, “Fuzzy partition and input selection by genetic algorithms for designing fuzzy rule-based classification systems,” in V. W. Porto, N. Saravanan, D. Waagen and A. E. Eiben (eds.) Springer-Verlag Lecture Notes in Computer Science Vol. 1447, Evolutionary Programming IV (Proc. of 7th International Conference on Evolutionary Programming, San Diego, USA) pp. 407-416, March 1998.

[97] H. Ishibuchi and T. Nakashima, “A study on generating fuzzy classification rules using histograms,” Proc. of 2nd International Conference on Knowledge-Based Intelligent Electronic Systems, vol. 1, pp. 132-140, Adelaide, Australia, April 1998.

[98] H. Ishibuchi and M. Nii, “Improving the generalization ability of neural networks by interval arithmetic,” Proc. of 2nd International Conference on Knowledge-Based Intelligent Electronic Systems, vol. 1, pp. 231-236, Adelaide, Australia, April 1998.

[99] T. Murata, H. Ishibuchi, and M. Gen, “Neighborhood structure of genetic local search algorithms,” Proc. of 2nd International Conference on Knowledge-Based Intelligent Electronic Systems, vol. 2, pp. 259-263, Adelaide, Australia, April 1998.

[100] M. Nii and H. Ishibuchi, “Fuzzy arithmetic in neural networks for linguistic rule extraction,” Proc. of 2nd International Conference on Knowledge-Based Intelligent Electronic Systems, vol. 2, pp. 387-394, Adelaide, Australia, April 1998.

[101] T. Murata, H. Ishibuchi, and M. Gen, “Adjusting fuzzy partitions by genetic algorithms and histograms for pattern classification problems,” Proc. of 1998 IEEE International Conference on Evolutionary Computation, pp. 9-14, Anchorage, USA, May 1998.

[102] T. Nakashima and H. Ishibuchi, “GA-based approaches for finding the minimum reference set for nearest neighbor

classification,” Proc. of 1998 IEEE International Conference on Evolutionary Computation, pp. 709-713, Anchorage, USA, May 1998.

[103] T. Nakashima, H. Ishibuchi, and T. Murata, “Evolutionary algorithms for constructing linguistic rule-based systems for high-dimensional pattern classification problems,” Proc. of 1998 IEEE International Conference on Evolutionary Computation, pp. 752-757, Anchorage, USA, May 1998.

[104] C. H. Oh, T. Nakashima, and H. Ishibuchi, “Initialization of Q-values by fuzzy rules for accelerating Q-learning,” Proc. of 1998 IEEE International Conference on Neural Networks, pp. 2051-2056, Anchorage, USA, May 1998.

[105] H. Ishibuchi and T. Murata, “Multi-objective genetic local search for minimizing the number of fuzzy rules for pattern classification problems,” Proc. of 1998 IEEE International Conference on Fuzzy Systems, pp. 1100-1105, Anchorage, USA, May 1998.

[106] H. Ishibuchi, M. Nii, and I. B. Turksen, “Bidirectional bridge between neural networks and linguistic knowledge: Linguistic rule extraction and learning from linguistic rules,” Proc. of 1998 IEEE International Conference on Fuzzy Systems, pp. 1112-1117, Anchorage, USA, May 1998.

[107] H. Ishibuchi and M. Nii, “Fuzzification of input vectors for improving the generalization ability of neural networks,” Proc. of 1998 IEEE International Conference on Fuzzy Systems, pp. 1153-1158, Anchorage, USA, May, 1998.

[108] H. Ishibuchi and T. Nakashima, “Fuzzy classification with reject options by fuzzy if-then rules,” Proc. of 1998 IEEE International Conference on Fuzzy Systems, pp. 1452-1457, Anchorage, USA, May 1998.

[109] H. Ishibuchi and T. Nakashima, “Knowledge acquisition by fuzzy classifier systems for pattern classification problems,” Proc. of 2nd International Symposium on Soft Computing for Industry, CD-ROM Proceedings, Anchorage, USA, May 1998.

[110] H. Ishibuchi and T. Murata, “GA-based two-objective flowshop scheduling with interval processing time: Scheduling and rescheduling,” Proc. of 2nd International Symposium on Soft Computing for Industry, CD-ROM Proceedings, Anchorage, USA, May 1998.

[111] T. Murata, H. Ishibuchi, and M. Gen, “Scheduling criteria for interval flowshop scheduling problems,” Proc. of 2nd International Symposium on Soft Computing for Industry, CD-ROM Proceedings, Anchorage, USA, May 1998.

[112] T. Murata, H. Ishibuchi, and M. Gen, “Formulation of multi-objective fuzzy scheduling problems using OWA operator,”

Proc. of 1998 Japan-USA Symposium on Flexible Automation, pp. 891-894, Ohtsu, Shiga, Japan, July 1998.

[113] T. Murata and H. Ishibuchi, “Constructing multi-objective genetic local search algorithms for multi-objective flowshop

scheduling problems,” Proc. of 1998 Japan-USA Symposium on Flexible Automation, pp. 1353-1356, Ohtsu, Shiga,

Japan, July 1998.

[114] T. Murata, H. Ishibuchi, and M. Gen, “Random weights in multi-objective genetic algorithms,” Proc. of 2nd

International Conference on Engineering Design and Automation, CD-ROM Proceedings, Maui, Hawaii, USA, August

1998.

[115] H. Ishibuchi, T. Nakashima, and L. C. Jain, “Genetic local search for reference pattern selection in fuzzy nearest

neighbor classification,” Proc. of 2nd IEEE International Conference on Intelligent Processing Systems, pp. 77-81, Gold

Coast, Australia, August 4-7, 1998.

[116] H. Ishibuchi and T. Nakashima, “Designing compact fuzzy rule-based systems by selecting reference patterns in fuzzy

nearest neighbor classification,” Proc. of 5th International Conference on Soft Computing and Information/Intelligent

Systems, pp. 939-942, Iizuka, Japan, October 1998.

[117] H. Ishibuchi, M. Nii, and T. Nakashima, “Approaches to the design of classification systems from numerical data and

linguistic knowledge,” Proc. of 5th International Conference on Soft Computing and Information/Intelligent Systems, pp.

919-922, Iizuka, Japan, October 1998.

[118] H. Ishibuchi and M. Nii, “Minimizing the measurement cost in the classification of new samples by neural-network

based classifiers,” Proc. of 5th International Conference on Soft Computing and Information/Intelligent Systems, pp.

634-637, Iizuka, Japan, October 1998.

[119] C. H. Oh, T. Nakashima, and H. Ishibuchi, “Performance evaluation of fuzzy rule-based Q-learning,” Proc. of 5th

International Conference on Soft Computing and Information/Intelligent Systems, pp. 195-198, Iizuka, Japan, October,

1998.

[120] T. Nakari, T. Nakashima, and H. Ishibuchi, “Evolution of strategies in an Iterated Prisoner’s Dilemma game with spatial

players, Proc. of 5th International Conference on Soft Computing and Information/Intelligent Systems, pp. 821-824,

Iizuka, Japan, October 1998.

[121] H. Ishibuchi, T. Murata, and T. Sotani, “Fuzzy rule selection by genetic local search for pattern classification problem,”

Proc. of 5th International Conference on Soft Computing and Information/Intelligent Systems, pp. 923-926, Iizuka, Japan,

October 1998.

[122] T. Murata, H. Ishibuchi, and M. Gen, “Multi-objective fuzzy scheduling problems considering job satisfaction goals,”

Proc. of 1st China-Japan Joint Conference on Industrial Engineering and Management, pp. 149-154, Beijing, China,

October 1998.

[123] H. Ishibuchi, T. Nakashima, and T. Murata, “Formulation of a fuzzy rule selection problem for high-dimensional

classification problems,” Proc. of 6th International Conference on Fuzzy Theory & Technology, pp. 119-122, Research

Triangle Park, North Carolina, USA, October 1998.

[124] T. Murata, H. Ishibuchi, and M. Gen, “Multi-objective fuzzy scheduling problems considering job importance grades,”

Proc. of 2nd Japan-Australia Joint Workshop on Intelligent and Evolutionary Systems, pp. 188-193, Kyoto, Japan,

November 1998.

[125] H. Ishibuchi, C. H. Oh, and T. Nakashima, “Strategies of a market selection game,” Proc. of 2nd Asia-Pacific

Conference on Simulated Evolution and Learning, vol. 2, Posters, Canberra, Australia, November 1998.

[126] H. Ishibuchi and T. Nakashima, “Evolution of reference sets in nearest neighbor classification,” Proc. of 2nd

Asia-Pacific Conference on Simulated Evolution and Learning, vol. 1, Session 5, Canberra, Australia, November 1998.

[127] T. Nakari, T. Nakashima, and H. Ishibuchi, “Evolution of mutual cooperation in a spatial Iterated Prisoner’s Dilemma

game with generalized objective functions,” Proc. of 2nd Asia-Pacific Conference on Simulated Evolution and Learning,

vol. 2, Posters, Canberra, Australia, November 1998.

[128] K. Tanaka, M. Nii, and H. Ishibuchi, “Learning from linguistic rules and rule extraction for function approximation by

neural networks,” Proc. of 2nd Asia-Pacific Conference on Simulated Evolution and Learning, vol. 2, Session 15,

Canberra, Australia, November 1998.

[129] H. Ishibuchi, M. Nii, and K. Tanaka, “Linguistic rule extraction from neural networks for high-dimensional

classification problems,” Proc. of Complex Systems’98, pp. 210-218, Sydney, Australia, November 30 - December 3,

1998.

[130] H. Ishibuchi, C. H. Oh, and T. Nakashima, “Competition between strategies of a market selection game,” Proc. of

Complex Systems’98, pp. 272-281, Sydney, Australia, November 30 - December 3, 1998.

[131] H. Ishibuchi, “A fuzzy reasoning method for handling fuzzy rules with different specificity levels,” Proc. of 18th

International Conference of the North American Fuzzy Information Processing Society – NAFIPS, pp. 110-114, New

York, USA, June 1999.

[132] H. Ishibuchi, T. Sotani, and T. Murata, “Tradeoff between the performance of fuzzy rule-based classification systems

and the number of fuzzy if-then rules,” Proc. of 18th International Conference of the North American Fuzzy Information

Processing Society – NAFIPS, pp. 125-129, New York, USA, June 1999.

[133] T. Nakashima and H. Ishibuchi, “Learning of fuzzy reference sets in nearest neighbor classification,” Proc. of 18th

International Conference of the North American Fuzzy Information Processing Society – NAFIPS, pp. 357-360, New

York, USA, June 1999.

[134] H. Ishibuchi, M. Nii, and K. Tanaka, “Subdivision methods for decreasing excess fuzziness of fuzzy arithmetic in

fuzzified neural networks,” Proc. of 18th International Conference of the North American Fuzzy Information Processing

Society – NAFIPS , pp. 448-452, New York, USA, June 1999.

[135] H. Ishibuchi, C. H. Oh, and T. Nakashima, “Designing a decision making system for a market selection game,” Proc. of

Computing in Economics and Finance ’99, on the web http://fmwww.bc.edu/CEF99/doc/pgm.html, Boston, USA, June

24-26, 1999.

[136] H. Ishibuchi, T. Nakari, and T. Nakashima, “Evolution of neighborly relations in a spatial IPD game with cooperative

players and hostile players,” Proc. of Congress on Evolutionary Computation, pp. 929-936, Washington D.C., USA, July

6-9, 1999.

[137] H. Ishibuchi and T. Nakashima, “Designing compact fuzzy rule-based systems with default hierarchies for linguistic

approximation,” Proc. of Congress on Evolutionary Computation, pp. 2341-2348, Washington D.C., USA, July 6-9,

1999.

[138] H. Ishibuchi, M. Nii, and K. Tanaka, “Decreasing excess fuzziness in fuzzy outputs from neural networks for linguistic

rule extraction,” Proc. of International Joint Conference on Neural Networks, CD-ROM Proceedings, Washington D.C.,

USA, July 10-16, 1999.

[139] T. Murata, H. Ishibuchi, and M. Gen, “Specification of genetic search directions in genetic local search algorithms for

multi-objective optimization problems,” Proc. of Genetic and Evolutionary Computation Conference, pp. 441-448,

Orlando, USA, July 13-17, 1999.

[140] H. Ishibuchi, “Fuzzy reasoning method in fuzzy rule-based systems with general and specific rules for function

approximation,” Proc. of IEEE International Conference on Fuzzy Systems, pp. 198-203, Seoul, Korea, August 22-25,

1999.

[141] T. Murata, H. Ishibuchi, and M. Gen, “Multi-objective fuzzy scheduling with the OWA operator for handling different

scheduling criteria and different job importance,” Proc. of IEEE International Conference on Fuzzy Systems, pp. 773-778,

Seoul, Korea, August 22-25, 1999.

[142] H. Ishibuchi and T. Nakashima, “Genetic-algorithm-based approach to linguistic approximation of nonlinear functions

with many input variables,” Proc. of IEEE International Conference on Fuzzy Systems, pp. 779-784, Seoul, Korea,

August 22-25, 1999.

[143] H. Ishibuchi, M. Nii, and C. H. Oh, “Approximate realization of fuzzy mappings by regression models, neural networks

and rule-based systems,” Proc. of IEEE International Conference on Fuzzy Systems, pp. 939-944, Seoul, Korea, August

22-25, 1999.

[144] H. Ishibuchi, M. Nii, and K. Tanaka, “Fuzzy-arithmetic-based approach for extracting positive and negative linguistic

rules from trained neural networks,” Proc. of IEEE International Conference on Fuzzy Systems, pp. 1382-1387, Seoul,

Korea, August 22-25, 1999.

[145] H. Ishibuchi, T. Nakashima, and T. Kuroda, “A hybrid fuzzy genetics-based machine learning algorithm: Hybridization

of Michigan approach and Pittsburgh approach,” Proc. of IEEE International Conference on Systems, Man and

Cybernetics, vol. 1, pp. 296-301, Tokyo, Japan, October 12-15, 1999.

[146] H. Ishibuchi and T. Murata, “Local search procedures in a multi-objective genetic local search algorithm for scheduling

problems,” Proc. of IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 665-670, Tokyo, Japan,

October 12-15, 1999.

[147] H. Ishibuchi, T. Nakari, and T. Nakashima, “Implementation of genetic algorithms for a spatial IPD game with a

generalized objective function,” Proc. of IEEE International Conference on Systems, Man and Cybernetics, vol. 4,

248-253, Tokyo, Japan, October 12-15, 1999.

[148] T. Murata, H. Ishibuchi, and M. Gen, “Construction of fuzzy classification systems using multiple fuzzy rule tables,”

Proc. of IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 875-880, Tokyo, Japan, October

12-15, 1999.

[149] H. Ishibuchi, T. Nakashima, and T. Kuroda, “A fuzzy genetics-based machine learning method for designing linguistic

classification systems with high comprehensibility,” Proc. of 6th International Conference on Neural Information

Processing, pp. 597-602, Perth, Australia, November 16-20, 1999.

[150] H. Ishibuchi and T. Nakashima, “Genetic-algorithm-based approaches to the linguistic rule extraction from numerical

data,” Proc. of International Symposium on Nonlinear Theory and its Applications, pp. 49-52, Hawaii, USA, November

28 - December 2, 1999.

[151] H. Ishibuchi and T. Nakashima, “Effect of rule weights in fuzzy rule-based classification systems,” Proc. of 9th IEEE

International Conference on Fuzzy Systems, pp. 59-64, San Antonio, May 7-10, 2000.

[152] H. Ishibuchi, T. Nakashima, and T. Kuroda, “A hybrid fuzzy GBML algorithm for designing compact fuzzy rule-based

classification systems,” Proc. of 9th IEEE International Conference on Fuzzy Systems, pp. 706-711, San Antonio, May

7-10, 2000.

[153] H. Ishibuchi, T. Nakashima, and T. Kuroda, “Minimizing the number of fuzzy rules by fuzzy genetics-based machine

learning for pattern classification problems,” Proc. of 8th Conference on Information Processing and Management of

Uncertainty in Knowledge-Based Systems, pp. 96-103, Madrid, Spain, July 3-7, 2000.

[154] H. Ishibuchi and T. Nakashima, “Fuzzy and crisp partition of pattern spaces for extracting linguistic rules from

numerical data,” Proc. of 8th Conference on Information Processing and Management of Uncertainty in

Knowledge-Based Systems, pp. 1668-1675, Madrid, Spain, July 3-7, 2000.

[155] H. Ishibuchi and T. Nakashima, “Linguistic rule extraction by genetics-based machine learning,” Proc. of Genetic and

Evolutionary Computation Conference, pp. 195-202, Las Vegas, Nevada, USA, July 8-12, 2000.

[156] T. Murata, H. Ishibuchi, and M. Gen, “Cellular genetic local search for multi-objective optimization,” Proc. of Genetic

and Evolutionary Computation Conference, pp. 307-314, Las Vegas, Nevada, USA, July 8-12, 2000.

[157] H. Ishibuchi, T. Nakari, and T. Nakashima, “Evolution of strategies in spatial IPD games with structural demes,” Proc.

of Genetic and Evolutionary Computation Conference, pp. 817-824, Las Vegas, Nevada, USA, July 8-12, 2000.

[158] H. Ishibuchi and T. Nakashima, “Multi-objective pattern and feature selection by a genetic algorithm,” Proc. of Genetic

and Evolutionary Computation Conference, pp. 1069-1076, Las Vegas, Nevada, USA, July 8-12, 2000.

[159] H. Ishibuchi, D. Takeuchi, and T. Nakashima, “High-dimensional test problems of linguistic modeling and fuzzy

genetics-based machine learning,” Proc. of 6th International Conference on Soft Computing, pp. 60-67, Iizuka, Fukuoka,

Japan, October 1-4, 2000.

[160] H. Ishibuchi, K. Tanaka, and M. Nii, “Learning of membership and certainty grades in fuzzy rule-based classification

system,” Proc. of 6th International Conference on Soft Computing, pp. 361-368, Iizuka, Fukuoka, Japan, October 1-4,

2000.

[161] H. Ishibuchi, R. Sakamoto, M. Nii, and T. Nakashima, “Adaptation of fuzzy systems and neural networks in a

non-stationary market selection game,” Proc. of 6th International Conference on Soft Computing, pp. 396-403, Iizuka,

Fukuoka, Japan, October 1-4, 2000.

[162] T. Murata, H. Ishibuchi, and M. Gen, “Specification of genetic search directions in cellular multi-objective genetic

algorithm,” Proc. of 1st International Conference on Evolutionary Multi-Criterion Optimization (E. Zitzler et al. (eds.),

Lecture Notes in Computer Science 1993, Evolutionary Multi-Criterion Optimization, Springer, Berlin), pp. 82-95,

March 7-9, 2001.

[163] H. Ishibuchi, T. Nakashima, and T. Murata, “Multiobjective optimization in linguistic rule extraction from numerical

data,” Proc. 1st International Conference on Evolutionary Multi-Criterion Optimization (E. Zitzler et al. (eds.), Lecture

Notes in Computer Science 1993, Evolutionary Multi-Criterion Optimization, Springer, Berlin), pp. 588-602, March 7-9,

2001.

[164] H. Ishibuchi and T. Nakashima, “Three-objective optimization in linguistic function approximation,” Proc. of Congress

on Evolutionary Computation 2001, pp. 340-347, Seoul, Korea, May 27-30, 2001.

[165] H. Ishibuchi, R. Sakamoto, and T. Nakashima, “Effect of localized selection on the evolution of unplanned coordination

in a market selection game,” Proc. of Congress on Evolutionary Computation 2001, pp. 1011-1018, Seoul, Korea, May

27-30, 2001.

[166] H. Ishibuchi, T. Nakashima, and T. Yamamoto, “Fuzzy association rules for handling continuous attributes,” Proc. of

2001 IEEE International Symposium on Industrial Electronics, pp. 118-121, Pusan, Korea, June 2001.

[167] T. Murata, S. Kawakami, H. Nozawa, M. Gen, and H. Ishibuchi, “Three-objective genetic algorithms for designing

compact fuzzy rule-based systems for pattern classification problems,” Proc. of Genetic and Evolutionary Computation

Conference 2001, pp. 485-492, San Francisco, CA, USA, July 7-11, 2001.

[168] H. Ishibuchi, D. Takeuchi, and T. Nakashima, “GA-based approaches to linguistic modeling of nonlinear functions,”

Proc. of 9th IFSA World Congress and 20th NAFIPS International Conference, pp. 1229-1234, Vancouver, Canada, July

25-28, 2001.

[169] H. Ishibuchi, T. Nakashima, and M. Nii, “Learning of neural networks with GA-based instance selection,” Proc. of 9th

IFSA World Congress and 20th NAFIPS International Conference, pp. 2102-2107, Vancouver, Canada, July 25-28,

2001.

[170] T. Murata, T. Sugimoto, Y. Tsujimura, M. Gen, and H. Ishibuchi, “Rule conversion in knowledge acquisition for

flowshop scheduling problems,” Proc. of 9th IFSA World Congress and 20th NAFIPS International Conference, pp.

2417-2421, Vancouver, Canada, July 25-28, 2001.

[171] T. Nakashima and H. Ishibuchi, “Supervised and unsupervised fuzzy discretization of continuous attributes for pattern

classification problems,” Proc. of Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies,

pp. 32-36, Osaka, Japan, September 2001.

[172] H. Ishibuchi, T. Yamamoto, and T. Nakashima, “Fuzzy data mining: Effect of fuzzy discretization,” Proc. of 2001 IEEE

International Conference on Data Mining, pp. 241-248, San Jose, USA, November 29 - December 2, 2001.

[173] H. Ishibuchi, T. Yamamoto, and T. Nakashima, “Linguistic modeling for function approximation using grid partitions,”

Proc. of 10th IEEE International Conference on Fuzzy Systems, pp. 47-50, Melbourne, Australia, December 2001.

[174] H. Ishibuchi, R. Sakamoto, and T. Nakashima, “Adaptation of fuzzy rule-based systems for game playing,” Proc. of

10th IEEE International Conference on Fuzzy Systems, pp. 1448-1451, Melbourne, Australia, December 2001.

[175] H. Ishibuchi, T. Yamamoto, and T. Nakashima, “Determination of rule weights of fuzzy association rules,” Proc. of 10th

IEEE International Conference on Fuzzy Systems, pp. 1555-1558, Melbourne, Australia, December 2001.

[176] H. Ishibuchi and T. Yoshida, “Improving the performance of multi-objective genetic local search algorithms,” Proc. of

7th International Symposium on Artificial Life and Robotics, pp. 278-281, Oita, Japan, January 16-18, 2002.

[177] T. Nakashima, T. Ariyama, and H. Ishibuchi, “A study on several variants of cellular genetic algorithms,” Proc. of 7th

International Symposium on Artificial Life and Robotics, pp. 282-285, Oita, Japan, January 16-18, 2002.

[178] T. Nakashima, G. Nakai, and H. Ishibuchi, “Adjusting membership functions and reducing the number of features for

designing fuzzy classification systems,” Proc. of 7th International Symposium on Artificial Life and Robotics, pp.

381-384, Oita, Japan, January 16-18, 2002.

[179] H. Ishibuchi and T. Seguchi, “Adaptation of neural networks and fuzzy systems to environmental changes in a

multi-agent model,” Proc. of 7th International Symposium on Artificial Life and Robotics, pp. 614-617, Oita, Japan,

January 16-18, 2002.

[180] T. Nakashima, G. Nakai, and H. Ishibuchi, “Improving the performance of fuzzy classification systems by membership

function leaning and feature selection,” Proc. of 2002 IEEE International Conference on Fuzzy Systems, pp. 488-493,

Honolulu, USA, May 12-17, 2002.

[181] H. Ishibuchi and T. Yamamoto, “Comparison of heuristic rule weight specification methods,” Proc. of 2002 IEEE

International Conference on Fuzzy Systems, pp. 908-913, Honolulu, USA, May 12-17, 2002.

[182] H. Ishibuchi and T. Seguchi, “Successive adaptation of fuzzy rule-based systems in a multi-agent model,” Proc. of 2002

IEEE International Conference on Fuzzy Systems, pp. 1009-1014, Honolulu, USA, May 12-17, 2002.

[183] H. Ishibuchi and T. Yamamoto, “Performance evaluation of fuzzy partitions with different fuzzification grades,” Proc.

of 2002 IEEE International Conference on Fuzzy Systems, pp. 1198-1203, Honolulu, USA, May 12-17, 2002.

[184] T. Nakashima, G. Nakai, and H. Ishibuchi, “A Fuzzy rule-based system for ensembling classification systems,” Proc. of

2002 IEEE International Conference on Fuzzy Systems, pp. 1432-1437, Honolulu, USA, May 12-17, 2002.

[185] H. Ishibuchi and T. Seguchi, “Successive adaptation of neural networks in a multi-agent model,” Proc. of 2002

International Joint Conference on Neural Networks, pp. 2454-2459, Honolulu, USA, May 12-17, 2002.

[186] T. Murata, H. Nozawa, Y. Tsujimura, M. Gen, and H. Ishibuchi, “Effect of local search on the performance of cellular

multi-objective genetic algorithms for designing fuzzy rule-based classification systems,” Proc. of Congress on

Evolutionary Computation, pp. 663-668, Honolulu, USA, May 12-17, 2002.

[187] H. Ishibuchi, T. Yoshida, and T. Murata, “Selection of initial solutions for local search in multiobjective evolutionary

algorithm,” Proc. of Congress on Evolutionary Computation, pp. 950-955, Honolulu, USA, May 12-17, 2002.

[188] H. Ishibuchi and T. Yamamoto, “Fuzzy rule selection by data mining criteria and genetic algorithms,” Proc. of 2002

Genetic and Evolutionary Computation Conference, pp. 399-406, New York, USA, July 9-13, 2002.

[189] H. Ishibuchi, T. Yoshida, and T. Murata, “Balance between genetic search and local search in hybrid evolutionary

multi-criterion optimization algorithms,” Proc. of 2002 Genetic and Evolutionary Computation Conference, pp.

1301-1308, New York, USA, July 9-13, 2002.

[190] T. Nakashima, T. Ariyama, and H. Ishibuchi, “Knowledge extraction by fuzzy-rule-learning and its effectiveness in

decision support for futures trading,” Proc. of 1st International Conference on Soft Computing and Intelligent Systems,

24B3-4, Tsukuba, Japan, October 21-25, 2002.

[191] T. Nakashima, G. Nakai, and H. Ishibuchi, “A genetic algorithm for constructing fuzzy classifier ensembles,” Proc. of

1st International Conference on Soft Computing and Intelligent Systems, 24P1-3, Tsukuba, Japan, October 21-25, 2002.

[192] T. Nakashima, M. Udo, and H. Ishibuchi, “Fuzzy Q-learning for acquiring the behavior of a soccer agent,” Proc. of 1st

International Conference on Soft Computing and Intelligent Systems, 24Q1-3, Tsukuba, Japan, October 21-25, 2002.

[193] T. Nakashima, T. Ariyama, and H. Ishibuchi, “On-line learning of a fuzzy system for a futures market,” Proc. of 2002

International Conference on Fuzzy Systems and Knowledge Discovery, pp. 54-58, Singapore, Singapore, November

18-22, 2002.

[194] T. Nakashima, G. Nakai, and H. Ishibuchi, “A boosting algorithm of fuzzy rule-based systems for pattern classification

problems,” Proc. of 2002 International Conference on Fuzzy Systems and Knowledge Discovery, pp. 155-158, Singapore,

Singapore, November 18-22, 2002.

[195] T. Nakashima, T. Ariyama, and H. Ishibuchi, “Combining multiple cellular genetic algorithms for efficient search,”

Proc. of 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp. 712-716, Singapore, Singapore,

November 18-22, 2002.

[196] H. Ishibuchi and T. Yoshida, “Implementation of local search in hybrid multi-objective genetic algorithms: A case study

on flowshop scheduling,” Proc. of 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp. 193-197,

Singapore, Singapore, November 18-22, 2002.

[197] H. Ishibuchi and T. Yamamoto, “Comparison of fuzzy rule selection criteria for classification problems,” Proc. of 2nd

International Conference on Hybrid Intelligent Systems, pp. 132-141, Santiago, Chile, December 1-4, 2002.

[198] H. Ishibuchi and T. Yoshida, “Hybrid evolutionary multi-objective optimization algorithms,” Proc. of 2nd International

Conference on Hybrid Intelligent Systems, pp. 163-172, Santiago, Chile, December 1-4, 2002.

[199] H. Ishibuchi and Y. Shibata, “An empirical study on the effect of mating restriction on the search ability of EMO

algorithms,” Proc. of 2nd International Conference on Evolutionary Multi-Criterion Optimization, pp. 433-447, Faro,

Portugal, April 8-11, 2003.

[200] T. Murata, H. Nozawa, H. Ishibuchi, and M. Gen, “Modification of local search directions for non-dominated solutions

in cellular multiobjective genetic algorithms for pattern classification problems,” Proc. of 2nd International Conference

on Evolutionary Multi-Criterion Optimization, pp. 593-607, Faro, Portugal, April 8-11, 2003.

[201] H. Ishibuchi and T. Yamamoto, “Effects of three-objective genetic rule selection on the generalization ability of fuzzy

rule-based systems,” Proc. of 2nd International Conference on Evolutionary Multi-Criterion Optimization, pp. 608-622,

Faro, Portugal, April 8-11, 2003.

[202] T. Yamamoto and H. Ishibuchi, “Performance evaluation of three-objective genetic rule selection,” Proc. of 2003 IEEE

International Conference on Fuzzy Systems, pp. 149-154, St. Louis, USA, May 25-28, 2003.

[203] T. Nakashima, M. Udo, and H. Ishibuchi, “Implementation of fuzzy Q-learning for a soccer agent,” Proc. of 2003 IEEE


[204] T. Nakashima, G. Nakai, and H. Ishibuchi, “A boosting algorithm with subset selection of training patterns,” Proc. of

2003 IEEE International Conference on Fuzzy Systems, pp. 690-695, St. Louis, USA, May 25-28, 2003.

[205] T. Nakashima, T. Ariyama, and H. Ishibuchi, “Extracting linguistic knowledge and its use as decision support in a

virtual futures market,” Proc. of 2003 IEEE International Conference on Fuzzy Systems, pp. 708-713, St. Louis, USA,

May 25-28, 2003.

[206] H. Ishibuchi and T. Yamamoto, “Deriving fuzzy discretization from interval discretization,” Proc. of 2003 IEEE


[207] H. Ishibuchi and Y. Shibata, “A similarity-based mating scheme for evolutionary multiobjective optimization,” Proc. of

2003 Genetic and Evolutionary Computation Conference, pp. 1065-1076, Chicago, USA, July 12-16, 2003.

[208] H. Ishibuchi and T. Yamamoto, “Evolutionary multiobjective optimization for generating an ensemble of fuzzy

rule-based classifiers,” Proc. of 2003 Genetic and Evolutionary Computation Conference, pp. 1077-1088, Chicago, USA,

July 12-16, 2003.

[209] T. Murata, S. Kaige, and H. Ishibuchi, “Generalization of dominance relation-based replacement rules for memetic EMO

algorithms,” Proc. of 2003 Genetic and Evolutionary Computation Conference, pp. 1234-1245, Chicago, USA, July

12-16, 2003.

[210] H. Ishibuchi and S. Kaige, “Comparison of multiobjective memetic algorithms on 0/1 knapsack problems,” Proc. of

2003 Genetic and Evolutionary Computation Conference Workshop Program, pp. 222-227, Chicago, USA, July 12,

2003.

[211] T. Nakashima, T. Ariyama, T. Yoshida, and H. Ishibuchi, “Performance evaluation of combined cellular genetic

algorithms for function optimization problems,” Proc. of 2003 IEEE International Symposium on Computational

Intelligence in Robotics and Automation, pp. 295-299, Kobe, Japan, July 16-20, 2003.

[212] T. Nakashima, G. Nakai, and H. Ishibuchi, “Credit assignment by fuzzy rule-based systems in fuzzy classifier

ensembles,” Proc. of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp.

664-669, Kobe, Japan, July 16-20, 2003.

[213] T. Nakashima, M. Udo, and H. Ishibuchi, “Acquiring the positioning skill in a soccer game using a fuzzy Q-learning,”

Proc. of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 1488-1491,

Kobe, Japan, July 16-20, 2003.

[214] T. Nakashima, M. Udo, and H. Ishibuchi, “A fuzzy reinforcement learning for a ball interception problem,” Proc. of

RoboCup 2003 Symposium, in CD-ROM (8 pages), Padova, Italy, July 10-11, 2003.

[215] T. Nakashima, T. Ariyama, and H. Ishibuchi, “Development and analysis of autonomously trading agents,” Proc. of

34th Annual Conference of the International Simulation and Gaming Association, pp. 883-892, Chiba, Japan, August

25-28, 2003.

[216] S. Kaige, T. Murata, and H. Ishibuchi, “Performance evaluation of memetic EMO algorithms using dominance

relation-based replacement rules on MOO test problems,” Proc. of 2003 IEEE International Conference on Systems, Man

and Cybernetics, pp. 14-19, Washington, D.C., USA, October 5-8, 2003.

[217] T. Nakashima, G. Nakai, and H. Ishibuchi, “Constructing fuzzy ensembles for pattern classification problems,” Proc. of

2003 IEEE International Conference on Systems, Man and Cybernetics, pp. 3200-3205, Washington, D.C., USA,

October 5-8, 2003.

[218] T. Nakashima, M. Udo, and H. Ishibuchi, “Knowledge acquisition for a soccer agent by fuzzy reinforcement learning,”

Proc. of 2003 IEEE International Conference on Systems, Man and Cybernetics, pp. 4256-4261, Washington, D.C., USA,

October 5-8, 2003.

[219] H. Ishibuchi and S. Kaige, “Effects of repair procedures on the performance of EMO algorithms for multiobjective 0/1

knapsack problems,” Proc. of 2003 Congress on Evolutionary Computation, pp. 2254-2261, Canberra, Australia,

December 8-12, 2003.

[220] H. Ishibuchi and S. Kaige, “A simple but powerful multiobjective hybrid genetic algorithms,” Proc. of 3rd Hybrid

Intelligent Systems, pp. 244-251, Melbourne, Australia, December 15-17, 2003.

[221] T. Nakashima, T. Yoshida, and H. Ishibuchi, “Explicit diversity preservation in distributed genetic algorithms by fitness

modification,” Proc. of 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems,

in CD-ROM (6 pages), Singapore, December 15-18, 2003.

[222] T. Murata, H. Kitano, T. Nakashima, and H. Ishibuchi, “Application of a multi-agent model with pioneers and followers

to a day care center allocation problem,” Proc. of International Conference on Intelligent Technologies 2003, pp.

179-186, Chiang Mai, Thailand, December 17-19, 2003.

[223] T. Yamamoto and H. Ishibuchi, “Single-objective and multi-objective fuzzy rule selection for generating an ensemble of

fuzzy rule-based classifiers,” Proc. of International Conference on Intelligent Technologies 2003, pp. 334-343, Chiang

Mai, Thailand, December 17-19, 2003.

[224] Y. Shibata, S. Kaige, and H. Ishibuchi, “Controlling the balance between diversity and convergence through mating

schemes in evolutionary multiobjective optimization,” Proc. of International Conference on Intelligent Technologies

2003, pp. 344-353, Chiang Mai, Thailand, December 17-19, 2003.

[225] T. Nakashima, T. Yoshida, and H. Ishibuchi, “Keeping diversity of reference set for keeping diversity of population in

distributed genetic algorithms,” Proc. of 9th International Conference on Artificial Life and Robotics, pp. 263-266, Oita,

Japan, January 28-30, 2004.

[226] T. Nakashima, H. Kitano, and H. Ishibuchi, “Performance evaluation of randomly trading agents in a virtual futures

market,” Proc. of 3rd International Workshop on Agent-based Approaches in Economic and Social Complex Systems, pp.

252-232, Kyoto, Japan, May 27-29, 2004.

[227] H. Ishibuchi and K. Narukawa, “Performance evaluation of simple multiobjective genetic local search algorithms on

multiobjective 0/1 knapsack problems,” Proc. of 2004 Congress on Evolutionary Computation, pp. 441-448, Portland,

USA, June 19-23, 2004.

[228] H. Ishibuchi and K. Narukawa, “Some issues on the implementation of local search in evolutionary multiobjective

optimization,” Proc. of 2004 Genetic and Evolutionary Computation Conference, part I, pp. 1246-1258, Seattle, USA,

June 26-30, 2004.

[229] H. Ishibuchi and Y. Shibata, “Mating scheme for controlling the diversity-convergence balance for multiobjective

optimization,” Proc. of 2004 Genetic and Evolutionary Computation Conference, part I, pp. 1259-1271, Seattle, USA,

June 26-30, 2004.

[230] S. Kaige, K. Narukawa, and H. Ishibuchi, “Lamarckian repair and darwinian repair in EMO algorithms for

multiobjective 0/1 knapsack problems,” Proc. of 2004 Genetic and Evolutionary Computation Conference Late Breaking

Papers, 12p CD ROM, Seattle, USA, June 26-30, 2004.

[231] H. Ishibuchi and T. Yamamoto, “Heuristic extraction of fuzzy classification rules using data mining techniques: An

empirical study on benchmark data sets,” Proc. of 2004 IEEE International Conference on Fuzzy Systems, 6p CD ROM,

Budapest, Hungary, July 25-29, 2004.

[232] T. Nakashima, H. Ishibuchi, and A. Bargiela, “A study on weighting training patterns for fuzzy rule-based classification

systems,” Proc. of 1st International Conference on Modeling Decisions for Artificial Intelligence, pp. 60-69, Barcelona,

Spain, August 2-4, 2004.

[233] H. Ishibuchi and S. Namba, “Evolutionary multiobjective knowledge extraction for high-dimensional pattern

classification problems,” Proc. of 8th International Conference on Parallel Problem Solving from Nature, pp. 1123-1132,

Birmingham, UK, September 18-22, 2004.

[234] T. Nakashima, M. Takatani, M. Udo, and H. Ishibuchi, “An evolutionary approach for learning soccer strategies,” Proc.

of Joint 2nd International Conference on Soft Computing and Intelligent Systems and 5th International Symposium on

Advanced Intelligent Systems, 4p CD ROM, Yokohama, Japan, September 21-24, 2004.

[235] T. Nakashima, M. Takatani, M. Udo, and H. Ishibuchi, “An evolutionary approach for strategy learning in RoboCup

soccer,” Proc. of 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 2023-2028, Den Hague,

Netherlands, October 10-13, 2004.

[236] T. Nakashima, H. Kitano, and H. Ishibuchi, “Development of a fuzzy position controller for an autonomously trading

agent,” Proc. of 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 2338-2343, Den Hague,


[237] H. Ishibuchi and T. Yamamoto, “Multi-objective evolutionary design of fuzzy rule-based systems,” Proc. of 2004 IEEE

International Conference on Systems, Man and Cybernetics, pp. 2362-2367, Den Hague, Netherland, October 10-13,

2004.

[238] T. Nakashima, H. Ishibuchi, and A. Bargiela, “Constructing fuzzy classification systems from weighted training

patterns,” Proc. of 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 2386-2391, Den Hague,


[239] H. Ishibuchi, “Effects of crossover operations on the performance of EMO algorithms,” Proc. of Dagstuhl Seminar, vol.

04461, 8 pages (Schloss Dagstuhl, Wadern, Germany, November 7-12, 2004) http://drops.dagstuhl.de/portals/04461/

[240] H. Ishibuchi and K. Narukawa, “Comparison of local search implementation schemes in hybrid evolutionary

multiobjective optimization algorithms,” Proc. of 4th International Conference on Hybrid Intelligent Systems, pp.

404-409, Kitakyushu, Japan, December 5-8, 2004.

[241] T. Nakashima, H. Kitano, and H. Ishibuchi, “An off-line learning method for improving the performance of an

autonomously trading agent,” Proc. of 5th International Conference on Recent Advances in Soft Computing, pp. 110-115,

Nottingham, UK, December 16-18, 2004.

[242] T. Nakashima, M. Takatani, M. Udo, H. Ishibuchi, and M. Nii, “Evolution of strategies for simulated RoboCup soccer,”

Proc. of 5th International Conference on Recent Advances in Soft Computing, pp. 183-188, Nottingham, UK, December

16-18, 2004.

[243] T. Yoshida, T. Nakashima, and H. Ishibuchi, “Fitness modification in genetic algorithms for function optimization

problems,” Proc. of 10th International Symposium on Artificial Life and Robotics, 4p, CD ROM, Oita, Japan, February

4-6, 2005.

[244] S. Namba, Y. Nojima, and H. Ishibuchi, “Performance comparison between fuzzy rules and interval rules in rule-based

classification systems,” Proc. of 10th International Symposium on Artificial Life and Robotics, 4p, CD ROM, Oita, Japan,

February 4-6, 2005.

[245] H. Ishibuchi and K. Narukawa, “Recombination of similar parents in EMO algorithms,” Proc. of 3rd International

Conference on Evolutionary Multi-Criterion Optimization, pp. 265-279, Guanajuato, Mexico, March 9-11, 2005.

[246] Y. Nojima, K. Narukawa, S. Kaige, and H. Ishibuchi, “Effects of removing overlapping solutions on the performance of

the NSGA-II algorithm,” Proc. of 3rd International Conference on Evolutionary Multi-Criterion Optimization, pp.

341-354, Guanajuato, Mexico, March 9-11, 2005.

[247] H. Ishibuchi, S. Kaige, and K. Narukawa, “Comparison between Lamarckian and Baldwinian repair on multiobjective

0/1 knapsack problems,” Proc. of 3rd International Conference on Evolutionary Multi-Criterion Optimization, pp.

370-385, Guanajuato, Mexico, March 9-11, 2005.

[248] H. Ishibuchi and Y. Nojima, “Multiobjective fuzzy genetics-based machine learning,” Proc. of 1st Workshop on Genetic

Fuzzy Systems, pp. 10-15, Granada, Spain, March 17-19, 2005.

[249] K. Narukawa, Y. Nojima, and H. Ishibuchi, “Modification of evolutionary multiobjective optimization algorithms for

multiobjective design of fuzzy rule-based classification systems,” Proc. of 2005 IEEE International Conference on Fuzzy

Systems, pp. 809-814, Reno, USA, May 22-25, 2005. [250] H. Ishibuchi and Y. Nojima, “Comparison between fuzzy and interval partitions in evolutionary multiobjective design of

rule-based classification systems,” Proc. of 2005 IEEE International Conference on Fuzzy Systems, pp. 430-435, Reno, USA, May 22-25, 2005.

[251] T. Nakashima, Y. Yokota, H. Ishibuchi, and A. Bargiela, “Constructing fuzzy classification systems from weighted training patterns,” Proc. of 19th European Conference on Modelling and Simulation, pp. 91-96, Riga, Latvia, June 1-4, 2005.

[252] T. Nakashima, H. Kitano, and H. Ishibuchi, “Behavior visualization of autonomous trading agents,” Proc. of 19th European Conference on Modelling and Simulation, pp. 288-293, Riga, Latvia, June 1-4, 2005.

[253] T. Nakashima, Y. Yokota, H. Ishibuchi, and A. Bargiela, “Effect of data weighting methods on the performance of fuzzy classification systems,” Proc. of 2005 North American Fuzzy Information Processing Society Conference, pp. 216-221, Michigan, USA, June 22-25, 2005.

[254] H. Ishibuchi and K. Narukawa, “Comparison of evolutionary multiobjective optimization with reference solution-based single-objective approach,” Proc. of 2005 Genetic and Evolutionary Computation Conference, vol. 1, pp. 787-794, Washington DC, USA, June 25-29, 2005.

[255] H. Ishibuchi, K. Narukawa, and Y. Nojima, “An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization,” Proc. of 2005 Genetic and Evolutionary Computation Conference, vol. 1, pp. 817-824, Washington DC, USA, June 25-29, 2005.

[256] T. Nakashima, M. Takatani, M. Udo, H. Ishibuchi, and M. Nii, “Performance evaluation of an evolutionary method for RoboCup soccer strategies,” Proc. of RoboCup 2005 International Symposium, in CD-ROM (8pages), Osaka, Japan, July 18-19, 2005.

[257] H. Ishibuchi and N. Namikawa, “Evolution of cooperative behavior in the Iterated Prisoner’s Dilemma under random pairing in game playing,” Proc. of 2005 Congress on Evolutionary Computation, pp. 2637-2644, Edinburgh, UK, September 2-5, 2005.

[258] H. Ishibuchi and Y. Nojima, “Multiobjective formulations of fuzzy rule-based classification system design,” Proc. of 4th Conference of the European Society for Fuzzy Logic and Technology and 11 Rencontres Francophones sur la Logique Floue et ses Applications, pp. 285-290, Barcelona, Spain, September 7-9, 2005.

[259] T. Nakashima, Y. Yokota, H. Ishibuchi, and G. Schaefer, “Learning fuzzy if-then rules for pattern classification with weighted training patterns,” Proc. of 4th Conference of the European Society for Fuzzy Logic and Technology and 11 Rencontres Francophones sur la Logique Floue et ses Applications, pp. 1064-1069, Barcelona, Spain, September 7-9, 2005.

[260] S. Yokoyama, N. Namikawa, T. Nakashima, M. Udo, and H. Ishibuchi, “Developing a goal keeper for simulated RoboCup soccer and its performance evaluation,” Proc. of 3rd International Symposium on Autonomous Minirobots for

Research and Edutainment, pp. 75-80, Fukui, Japan, September 20-22, 2005. [261] Y. Yokota, T. Nakashima, H. Ishibuchi, and G. Schaefer, “Cost-sensitive approach to the construction of fuzzy

classification systems,” Proc. of 6th International Symposium on Advanced Intelligent Systems, pp. 412-417, Yeosu, Korea, September 28 - October 1, 2005.

[262] H. Kitano, T. Nakashima, and H. Ishibuchi, “Interpreting the behavior of a trading agent using fuzzy if-then rules,” Proc. of 6th International Symposium on Advanced Intelligent Systems, pp. 588-593, Yeosu, Korea, September 28 - October 1, 2005.

[263] M. Takatani, T. Nakashima, M. Udo, H. Ishibuchi, and M. Nii, “Robust evaluation using match history for evolution of soccer team strategies,” Proc. of 6th International Symposium on Advanced Intelligent Systems, pp. 669-674, Yeosu, Korea, September 28 - October 1, 2005.

[264] N. Namikawa, T. Nakashima, and H. Ishibuchi, “A study on robust evaluation schemes for game strategy evolution,” Proc. of 6th International Symposium on Advanced Intelligent Systems, pp. 718-722, Yeosu, Korea, September 28 - October 1, 2005.

[265] K. Narukawa, Y. Nojima, and H. Ishibuchi, “Effects of similarity-based mating scheme on evolutionary function optimization,” Proc. of 6th International Symposium on Advanced Intelligent Systems, pp. 735-740, Yeosu, Korea, September 28 - October 1, 2005.

[266] H. Kitano, T. Nakashima, and H. Ishibuchi, “Behavior analysis of futures trading agents using fuzzy rule extraction,”

Proc. of 2005 IEEE International Conference on Systems, Man and Cybernetics, pp. 1477-1481, Hawaii, USA, October

10-12, 2005. [267] Y. Nojima and H. Ishibuchi, “Comparison between single-objective and multi-objective formulations for fuzzy

rule-based classification system design,” Proc. of 5th Asian Symposium on Applied Electromagnetics and Mechanics, pp. 320-326, Hanoi, Vietnam, October 10-12, 2005.

[268] H. Ishibuchi and K. Narukawa, “Spatial implementation of evolutionary multiobjective algorithms with partial Lamarckian repair for multiobjective knapsack problems,” Proc. of 5th International Conference on Hybrid Intelligent Systems, pp. 265-270, Rio de Janeiro, Brazil, November 6-9, 2005.

[269] H. Ishibuchi and Y. Nojima, “Performance evaluation of evolutionary multiobjective approaches to the design of fuzzy rule-based ensemble classifiers,” Proc. of 5th International Conference on Hybrid Intelligent Systems, pp. 271-276, Rio de Janeiro, Brazil, November 6-9, 2005.

[270] H. Ishibuchi and Y. Nojima, “Accuracy-complexity tradeoff analysis by multiobjective rule selection,” Proc. of ICDM 2005 Workshop on Computational Intelligence in Data Mining, pp. 39-48, Houston, Texas, USA, November 27, 2005.

[271] H. Kitano, T. Nakashima, and H. Ishibuchi, “A genetic approach to the design of autonomous agents for futures trading,” Proc. of 11th International Symposium on Artificial Life and Robotics, pp. 406-409, Oita, Japan, January 23-25, 2006.

[272] T. Nakashima, Y. Yokota, H. Ishibuchi, and G. Schaefer, “Comparative study on fuzzy and non-fuzzy cost-sensitive classification systems,” Proc. of 11th International Symposium on Artificial Life and Robotics, pp. 582-585, Oita, Japan, January 23-25, 2006.

[273] Y. Nojima and H. Ishibuchi, “Interpretability-accuracy tradeoff by multiobjective genetics-based machine learning for pattern classification problems,” Proc. of 11th International Symposium on Artificial Life and Robotics, pp. 452-455, Oita, Japan, January 23-25, 2006.

[274] T. Nakashima, M. Takatani, H. Ishibuchi, and M. Nii, “The effect of using match history on the evolution of RoboCup soccer team strategies,” Proc. of 2006 IEEE Symposium on Computational Intelligence and Games, pp. 60-66, Reno/Lake Tahoe, Nevada, USA, May 22-24, 2006.

[275] T. Nakashima, Y. Yokota, G. Schaefer, and H. Ishibuchi, “Generating classification rules from numerical data with misclassification cost,” Proc. of 20th European Conference on Modelling and Simulation 2006, pp. 79-84, Bonn,

Germany, May 28-31, 2006. [276] H. Ishibuchi, Y. Nojima, and T. Doi, “Application of multiobjective evolutionary algorithms to single-objective

optimization problems,” Abstract Booklet of 7th International Conference on Multi-Objective Programming and Goal Programming, 4 pages, Tours, France, June 12-14, 2006.

[277] H. Ishibuchi and Y. Nojima, “Tradeoff between accuracy and rule length in fuzzy rule-based classification systems for high-dimensional problems,” Proc. of 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 1936-1943, Paris, France, July 2-7, 2006.

[278] K. Ohara, Y. Nojima, and H. Ishibuchi, “Comparison between centralized global optimization and distributed local

optimization for traffic jam avoidance,” Proc. of 2006 Genetic and Evolutionary Computation Conference Late Breaking

Papers, 6p CD ROM, Seattle, USA, July 8-12, 2006. [279] H. Ishibuchi, Y. Nojima, K. Narukawa, and T. Doi, “Incorporation of decision maker’s preference into evolutionary

multiobjective optimization algorithms,” Proc. of 2006 Genetic and Evolutionary Computation Conference, vol. 1, pp. 741-742, Seattle, USA, July 8-12, 2006.

[280] H. Ishibuchi, Y. Nojima, and I. Kuwajima, “Multiobjective genetic rule selection as a data mining postprocessing procedure,” Proc. of 2006 Genetic and Evolutionary Computation Conference, vol. 2, pp. 1591-1592, Seattle, USA, July 8-12, 2006.

[281] T. Nakashima, Y. Yokota, G. Schaefer, and H. Ishibuchi, “A cost-based fuzzy rule-based system for pattern classification problems,” Proc. of 2006 IEEE International Conference on Fuzzy Systems, pp. 847-851, Vancouver, Canada, July 16-21, 2006.

[282] H. Ishibuchi, N. Namikawa, and K. Ohara, “Effects of spatial structures on evolution of Iterated Prisoner’s Dilemma game strategies in single-dimensional and two-dimensional grids,” Proc. of 2006 Congress on Evolutionary Computation, pp. 3721-3728, Vancouver, Canada, July 16-21, 2006.

[283] H. Ishibuchi, Y. Nojima, and T. Doi, “Comparison between single-objective and multi-objective genetic algorithms: Performance comparison and performance measures,” Proc. of 2006 Congress on Evolutionary Computation, pp. 3959-3966, Vancouver, Canada, July 16-21, 2006.

[284] T. Nakashima, M. Takatani, N. Namikawa, H. Ishibuchi, and M. Nii, “Robust evaluation of RoboCup soccer strategies by using match history,” Proc. of 2006 Congress on Evolutionary Computation, pp. 4338-4344, Vancouver, Canada, July 16-21, 2006.

[285] H. Ishibuchi, Y. Nojima, and I. Kuwajima, “Fuzzy data mining by heuristic rule extraction and multiobjective genetic rule selection,” Proc. of 2006 IEEE International Conference on Fuzzy Systems, pp. 7824-7831, Vancouver, Canada, July 16-21, 2006.

[286] Y. Nojima, H. Ishibuchi, and I. Kuwajima, “Comparison of search ability between genetic fuzzy rule selection and fuzzy genetics-based machine learning,” Proc. of 2006 International Symposium on Evolving Fuzzy Systems, pp. 125-130, Ambleside, Lake District, UK, September 7-9, 2006.

[287] H. Ishibuchi, Y. Nojima, and I. Kuwajima, “Genetic rule selection as a postprocessing procedure in fuzzy data mining,” Proc. of 2006 International Symposium on Evolving Fuzzy Systems, pp. 286-291, Ambleside, Lake District, UK, September 7-9, 2006.

[288] H. Ishibuchi, T. Doi, and Y. Nojima, “Incorporation of scalarizing fitness functions into evolutionary multiobjective

optimization algorithms,” Proc. of 9th International Conference on Parallel Problem Solving from Nature, pp. 493-502,

Reykjavik, Iceland, September 9-13, 2006.

[289] H. Ishibuchi, T. Doi, and Y. Nojima, “Effects of using two neighborhood structures in cellular genetic algorithms for

function optimization,” Proc. of 9th International Conference on Parallel Problem Solving from Nature, pp. 949-958,

Reykjavik, Iceland, September 9-13, 2006.

[290] H. Ishibuchi, I. Kuwajima, and Y. Nojima, “Multiobjective association rule mining,” Proc. of PPSN Workshop on

Multiobjective Problem Solving from Nature (September 9, 2006) 12 pages (Reykjavik, Iceland)

http://dbkgroup.org/knowles/MPSN3/

[291] N. Namikawa, T. Nakashima, and H. Ishibuchi, “Constructing a mimicking soccer agent using neural networks,” Proc.

of Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on

Advanced Intelligent Systems, pp. 69-74, Tokyo, Japan, September 2006.

[292] Y. Yokota, T. Nakashima, and H. Ishibuchi, “Performance evaluation of cost-sensitive fuzzy classification systems for

breast cancer diagnosis,” Proc. of Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th

International Symposium on Advanced Intelligent Systems, pp. 1014-1019, Tokyo, Japan, September 2006.

[293] H. Ishibuchi, Y. Nojima, and I. Kuwajima, “Accuracy-complexity tradeoff analysis in data mining by multiobjective

genetic rule selection,” Proc. of Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th

International Symposium on Advanced Intelligent Systems, pp. 2069-2074, Tokyo, Japan, September 2006.

[294] H. Ishibuchi, Y. Nojima, and T. Doi, “Driving evolutionary multiobjective search by a scalarizing fitness function,”

Proc. of Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium

on Advanced Intelligent Systems, pp. 2198-2203, Tokyo, Japan, September 2006.

[295] H. Ishibuchi, Y. Nojima, and I. Kuwajima, “Finding simple fuzzy classification systems with high interpretability

through multiobjective rule selection,” Proc. of 10th International Conference on Knowledge Based Intelligent

Information and Engineering Systems, pp. 86-93, Bournemouth, UK, October 9-11, 2006.

[296] T. Nakashima, Y. Yokota, G. Schaefer, and H. Ishibuchi, “Examining the effect of cost assignment on the performance

of cost-based classification systems,” Proc. of 2006 IEEE International Conference on Systems, Man, and Cybernetics -

SMC 2006, pp. 2772-2777, Taipei, Taiwan, October 8-11, 2006.

[297] Y. Nojima and H. Ishibuchi, “Designing fuzzy ensemble classifiers by evolutionary multiobjective optimization with an

entropy-based diversity criterion,” Proc. of 6th International Conference on Hybrid Intelligent Systems and 4th

Conference on Neuro-Computing and Evolving Intelligence, in CD-ROM (4 pages), Auckland, New Zealand, December

13-15, 2006.

[298] H. Ishibuchi, K. Ohara, and Y. Nojima, “Elitism in cellular genetic algorithms with two neighborhood structures,”

Abstract Booklet of 9th Workshop on Foundation of Genetic Algorithms, 12 pages, Mexico City, Mexico, January 7-11,

2007.

[299] T. Nakashima, H. Ishibuchi , and M. Nii, “Knowledge evolution in a dynamic environment of RoboCup simulation,”

Proc. of the 12th International Symposium on Artificial Life and Robotics, in CD-ROM (4 pages), Oita, Japan, January

25-27, 2007.

[300] T. Nakashima, Y. Yokota, H. Ishibuchi, and G. Schaefer, “A cost-based fuzzy system for pattern classification with

class importance,” Proc. of 12th International Symposium on Artificial Life and Robotics, in CD-ROM (4 pages), Oita,

Japan, January 25-27, 2007.

[301] H. Ishibuchi and Y. Nojima, “Optimization of scalarizing functions through evolutionary multiobjective optimization,”

Proc. of 4th International Conference on Evolutionary Multi-Criterion Optimization, pp. 51-65, Matsushima, Japan,

March 5-8, 2007.

[302] H. Ishibuchi, I. Kuwajima, and Y. Nojima, “Relation between Pareto-optimal fuzzy rules and Pareto-optimal fuzzy rule

sets,” Proc. of 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making, pp. 42-49,

Honolulu, USA, April 1-5, 2007.

[303] H. Ishibuchi, “Evolutionary multiobjective design of fuzzy rule-based systems,” Proc. of 2007 IEEE Symposium on

Foundation of Computational Intelligence, pp. 9-16, Honolulu, USA, April 1-5, 2007.

[304] G. Schaefer, T. Nakashima, Y. Yokota, and H. Ishibuchi, “Cost-Sensitive fuzzy classification for medical diagnosis,”

Proc. of 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp.

312-316, Honolulu, USA, April 1-5, 2007.

[305] T. Nakashima, Y. Yokota, G. Schaefer, and H. Ishibuchi, “Performance evaluation of fuzzy rule-based systems with

class priority for medical diagnosis problem,” Proc. of 21st European Conference on Modelling and Simulation, June 4-6,

pp. 283-288, Prague, Czech Republic, 2007. Invited Talk

[306] H. Ishibuchi, I. Kuwajima, and Y. Nojima, “Use of Pareto-optimal and near Pareto-optimal candidate rules in genetic

fuzzy rule selection,” 2007 IFSA World Congress, 10 pages, Cancun, Mexico, June 18-21, 2007. [307] H. Ishibuchi, Y. Nojima, N. Tsukamoto, and K. Ohara, “Effects of the use of non-geometric binary crossover on

evolutionary multiobjective optimization,” Proc. of 2007 Genetic and Evolutionary Computation Conference, vol. 1, pp. 829-836, London, UK, July 7-11, 2007.

[308] H. Ishibuchi, “Multiobjective genetic fuzzy systems: Review and future research directions,” Proc. of 2007 IEEE

International Conference on Fuzzy Systems, pp. 913-918, London, UK, July 23-26, 2007.

[309] G. Schaefer, T. Nakashima, Y. Yokota, and H. Ishibuchi, “Fuzzy classification of gene expression data,” Proc. of 2007

IEEE International Conference on Fuzzy Systems, pp. 1090-1095, London, UK, July 23-26, 2007.

[310] G. Schaefer, T. Nakashima, M. Zavisek, Y. Yokota, A. Drastich, and H. Ishibuchi, “Breast cancer classification using

statistical features and fuzzy classification of thermograms,” Proc. of 2007 IEEE International Conference on Fuzzy

Systems, pp. 1096-1100, London, UK, July 23-26, 2007.

[311] T. Nakashima, Y. Yokota, G. Schaefer, and H. Ishibuchi, “Introducing class-based classification priority in fuzzy

rule-based classification systems,” Proc. of 2007 IEEE International Conference on Fuzzy Systems, pp. 1757-1762,

London, UK, July 23-26, 2007.

[312] Y. Nojima, I. Kuwajima, and H. Ishibuchi, “Data set subdivision for parallel distributed implementation of genetic fuzzy

rule selection,” Proc. of 2007 IEEE International Conference on Fuzzy Systems, pp. 2006-2011, London, UK, July 23-26,

2007.

[313] H. Ishibuchi, “Evolutionary multiobjective optimization for fuzzy knowledge extraction,” Proc. of 8th International

Symposium on Advanced Intelligent Systems, pp. 58-63, Sokcho, Korea, September 5-8, 2007. Invited Talk

[314] H. Ishibuchi, K. Ohara, and Y. Nojima, “Implementation of elitism in cellular genetic algorithms,” Proc. of 8th

International Symposium on Advanced Intelligent Systems, pp. 169-174, Sokcho, Korea, September 5-8, 2007.

[315] Y. Hamada, Y. Nojima, K. Ohara, and H. Ishibuchi, “A simulation study of route selection with inter-vehicle

communication,” Proc. of 8th International Symposium on Advanced Intelligent Systems, pp. 175-180, Sokcho, Korea,

September 5-8, 2007.

[316] Y. Hitotsuyanagi, Y. Nojima, and Hisao Ishibuchi, “Effects of problem-specific local search schemes in a memetic EMO

algorithm,” Proc. of 8th International Symposium on Advanced Intelligent Systems, pp. 402-407, Sokcho, Korea,


[317] Y. Shoji, T. Nakashima, and H. Ishibuchi, “Relation between the performance of ensemble classification systems and

the diversity of classification systems,” Proc. of 8th International Symposium on Advanced Intelligent Systems, pp.

806-811, Sokcho, Korea, September 5-8, 2007.

[318] H. Ishibuchi, I. Kuwajima, and Y. Nojima, “Prescreening of candidate rules using association rule mining and

Pareto-optimality in genetic rule selection,” Proc. of 11th International Conference on Knowledge Based Intelligent

Information and Engineering Systems, pp. 509-516, Vietri sul Mare, Italy, September 12-14, 2007.

[319] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Iterative approach to indicator-based multiobjective optimization,” Proc.

of 2007 IEEE Congress on Evolutionary Computation, pp. 3697-3704, Singapore, September 25-28, 2007.

[320] H. Ishibuchi, Y. Hitotsuyanagi, and Y. Nojima, “An empirical study on the specification of the local search application

probability in multiobjective memetic algorithms,” Proc. of 2007 IEEE Congress on Evolutionary Computation, pp.

2788-2795, Singapore, September 25-28, 2007.

[321] K. Ohara, Y. Nojima, and H. Ishibuchi, “Effects of spatial structures on evolution of Iterated Prisoner's Dilemma game

strategies with probabilistic decision making,” Proc. of 2007 IEEE Congress on Evolutionary Computation, pp.

4051-4058, Singapore, September 25-28, 2007.

[322] H. Ishibuchi, “Hot issues in evolutionary multiobjective optimization,” 2007 IEEE Congress on Evolutionary

Computation, Singapore, September 25-28, 2007. Invited Talk

[323] T. Nakashima and H. Ishibuchi, “Mimicking dribble trajectories by neural networks for RoboCup soccer simulation,”

Proc. of 2007 IEEE Multi-conference on Systems and Control, pp.658-663, Singapore, October 1-3, 2007.

[324] K. Ohara, Y. Nojima, and H. Ishibuchi, “A study on traffic information sharing through inter-vehicle communication,”

Proc. of 2007 IEEE Multi-conference on Systems and Control, pp. 670-675, Singapore, October 1-3, 2007.

[325] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Choosing extreme parents for diversity improvement in evolutionary

multiobjective optimization algorithms,” Proc. of 2007 IEEE International Conference on Systems, Man and Cybernetics,

pp. 1946-1951, Montreal, Canada, October 7-10, 2007.

[326] I. Kuwajima, Y. Nojima, and H. Ishibuchi, “Obtaining accurate classifiers with Pareto-optimal and near Pareto-optimal

rules,” Proc. of 13th International Symposium on Artificial Life and Robotics, pp. 195-198, Oita, Japan, January

31-February 2, 2008.

[327] I. Kuwajima, Y. Nojima, and H. Ishibuchi, “Effects of constructing fuzzy discretization from crisp discretization for

rule-based classifiers,” Proc. of 13th International Symposium on Artificial Life and Robotics, pp. 203-206, Oita, Japan,

January 31-February 2, 2008.

[328] N. Tsukamoto, Y. Nojima, and H. Ishibuchi, “Effects of non-geometric binary crossover on multiobjective 0/1 knapsack

problems,” Proc. of 13th International Symposium on Artificial Life and Robotics, pp. 642-645, Oita, Japan, January

31-February 2, 2008.

[329] H. Ishibuchi, Y. Kaisho, and Y. Nojima, “Designing fuzzy rule-based classifiers that can visually explain their

classification results to human users,” Proc. of 3rd International Workshop on Genetic and Evolving Fuzzy Systems, pp.

5-10, Witten-Bommerholz, Germany, March 4-7, 2008.

[330] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Evolutionary many-objective optimization,” Proc. of 3rd International

Workshop on Genetic and Evolving Fuzzy Systems, pp. 47-52, Witten-Bommerholz, Germany, March 4-7, 2008.

[331] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Behavior of evolutionary many-objective optimization,” Proc. of 10th

International Conference on Computer Modeling and Simulation, pp. 266-271, Cambridge, UK, April 1-3, 2008.

[332] H. Ishibuchi, Y. Kaisho, and Y. Nojima, “A visual explanation system for explaining fuzzy reasoning results by fuzzy

rule-based classifiers,” Proc. of 2008 North American Fuzzy Information Processing Society Conference, in CD-ROM (6

pages), New York, USA, May 19-22, 2008.

[333] H. Ishibuchi, Y. Hitotsuyanagi, and Y. Nojima, “Scalability of multiobjective genetic local search to many-objective

problems: Knapsack problem case studies,” Proc. of 2008 IEEE Congress on Evolutionary Computation, pp. 3587-3594,

Hong Kong, June 1-6, 2008.

[334] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Evolutionary many-objective optimization: A short review,” Proc. of 2008

IEEE Congress on Evolutionary Computation, pp. 2424-2431, Hong Kong, June 1-6, 2008.

[335] S. Fujii, T. Nakashima, and H. Ishibuchi, “A Study on constructing fuzzy systems for high-level decision making in a

car racing game,” Proc. of 2008 IEEE International Conference on Fuzzy Systems, pp. 2299-2306, Hong Kong, June 1-6,

2008.

[336] I. Kuwajima, H. Ishibuchi, and Y. Nojima, “Effectiveness of designing fuzzy rule-based classifiers from Pareto-optimal

rules,” Proc. of 2008 IEEE International Conference on Fuzzy Systems, pp. 1185-1192, Hong Kong, June 1-6, 2008.

[337] Y. Nojima, H. Ishibuchi, “Computational efficiency of parallel distributed genetic fuzzy rule selection for large data

sets,” Proc. of Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 1137-1142,

Torremolinos, Spain, June 22-27, 2008.

[338] H. Ishibuchi, N. Tsukamoto, Y. Hitotsuyanagi, and Y. Nojima, “Effectiveness of scalability improvement attempts on

the performance of NSGA-II for many-objective problems”, Proc. of 2008 Genetic and Evolutionary Computation

Conference, pp. 649-656, Atlanta, Georgia, USA, July 12-16, 2008.

[339] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Maintaining the diversity of solutions by non-geometric binary crossover:

A worst one-max solver competition case study”, Proc. of 2008 Genetic and Evolutionary Computation Conference, pp.

1111-1112, Atlanta, Georgia, USA, July 12-16, 2008.

[340] Y. Hitotsuyanagi, Y. Nojima, and H. Ishibuchi, “Balance between Local Search and Global Search in Multiobjective

Memetic Algorithms for Many-Objective Optimization Problems”, Workshop and Summer School on Evolutionary

Computing Lecture Series by Pioneers, pp. 22-25, Derry, Northern Ireland, August 18-22, 2008.

[341] H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, “Probabilistic use of heuristic moves in multiobjective

genetic local search for flowshop scheduling,” Conference Handbook of the UK Operational Research Society 50th

Annual Conference, p.115, York, UK, September 9-11, 2008.

[342] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Examining the effect of elitism in cellular genetic algorithms using two

neighborhood structures,” Proc. of 10th International Conference on Parallel Problem Solving from Nature, pp. 458-467,

Dortmund, Germany, September 13-17, 2008.

[343] H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, “Use of heuristic local search for single-objective

optimization in multiobjective memetic algorithms,” Proc. of 10th International Conference on Parallel Problem Solving

from Nature, pp. 743-752, Dortmund, Germany, September 13-17, 2008.

[344] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Maintaining the diversity of solutions by non-geometric binary crossover

in genetic algorithms,” Proc. of Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th

International Symposium on Advanced Intelligent Systems, pp. 1512-1517, Nagoya, Japan, September 17-21, 2008.

[345] Y. Nojima and H. Ishibuchi, “Effects of diversity measures on the design of ensemble classifiers by multiobjective

genetic fuzzy rule selection with a multi-classifier coding scheme,” Proc. of Third International Workshop on Hybrid

Artificial Intelligence Systems, pp. 755-762, Burgos, Spain, September 24-26, 2008.

[346] H. Ishibuchi, “Evolutionary multiobjective optimization and multiobjective fuzzy system design,” Proc. of 5th

International Conference on Soft Computing as Transdisciplinary Science and Technology, pp. 3-4, Cergy-Pontoise,

France, October 28-31, 2008. Keynote Speech

[347] H. Ishibuchi and Y. Nojima, “Evolutionary multiobjective fuzzy system design,” Proc. of 2nd Workshop on Computing

and Communications from Biological Systems: Theory and Applications, CD ROM Proceedings (2 pages), Awaji Island,

Japan, November 28, 2008.

[348] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Use of local ranking in cellular genetic algorithms with two neighborhood

structures,” Proc. of 7th International Conference on Simulated Evolution and Learning (SEAL 2008), pp. 309-318,

Melbourne, Australia, December 7-10, 2008.

[349] Y. Nojima, and H. Ishibuchi, “Incorporation of user preference into multiobjective genetic fuzzy rule selection for

pattern classification problems,” Proc. of 14th International Symposium on Artificial Life and Robotics, pp. 186-189, Oita,

Japan, February 5-7, 2009.

[350] I. Kuwajima, Y. Nojima, and H. Ishibuchi, “Pareto-optimal fuzzy rule mining with EMO algorithms and its

improvement by heuristic initialization,” Proc. of 14th International Symposium on Artificial Life and Robotics, pp.

377-380, Oita, Japan, February 5-7, 2009.

[351] N. Tsukamoto, Y. Sakane, Y. Nojima, and H. Ishibuchi, “Hybridization of evolutionary multiobjective optimization

algorithms by the adaptive use of scalarizing fitness function,” Proc. of 14th International Symposium on Artificial Life

and Robotics, pp. 365-368, Oita, Japan, February 5-7, 2009.

[352] Y. Hamada, Y. Nojima, and H. Ishibuchi, “Use of multiobjective genetic rule selection for examining the effectiveness

of inter-vehicle communication in traffic simulations,” Proc. of 14th International Symposium on Artificial Life and

Robotics, pp. 93-96, Oita, Japan, February 5-7, 2009.

[353] H. Ohyanagi, Y. Wakamatsu, Y. Nakashima, Y. Nojima, and H. Ishibuchi, “Evolution of cooperative behavior among

heterogeneous agents with different strategy representations in an Iterated Prisoner's Dilemma game,” Proc. of 14th

International Symposium on Artificial Life and Robotics, pp. 102-105, Oita, Japan, February 5-7, 2009.

[354] Y. Nojima, Y. Hamada, and H. Ishibuchi, “Application of interactive fuzzy data mining to the analysis of inter-vehicle

communication in traffic simulations,” Proc. of 5th International Conference on Sciences of Electronic, Technologies of

Information and Telecommunications, March 22-26, 2009, Hammamet, Tunisia (CD-ROM 11 pages).

[355] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Empirical analysis of using weighted sum fitness functions in NSGA-II

for many-objective 0/1 knapsack problems,” Proc. of 11th International Conference on Computer Modelling and

Simulation (UKSim 2009), pp. 71-76, Cambridge, UK, March 25-27, 2009.

[356] Y. Nojima and H. Ishibuchi, Interactive genetic fuzzy rule selection through evolutionary multiobjective optimization

with user preference, Proc. of 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making,

pp. 141-148, Nashville, USA, March 30-April 2, 2009.

[357] H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, “Adaptation of scalarizing functions in MOEA/D: An adaptive

scalarizing function-based multiobjective evolutionary algorithm,” Proc. of 5th International Conference on Evolutionary

Multi-Criterion Optimization, pp. 438-452, Nantes, France, April 7-10, 2009.

[358] H. Ishibuchi, N. Tsukamoto, Y. Sakane, and Y. Nojima, “Hypervolume approximation using achievement scalarizing

functions for evolutionary many-objective optimization,” Proc. of 2009 IEEE Congress on Evolutionary Computation, pp.

530-537, Trondheim, Norway, May 18, 2009.

[359] H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, “Effects of using two neighborhood structures on the

performance of cellular evolutionary algorithms for many-objective optimization,” Proc. of 2009 IEEE Congress on

Evolutionary Computation, pp. 2508-2515, Trondheim, Norway, May 18-21, 2009.

[360] H. Ishibuchi, “Evolutionary multiobjective optimization and fuzzy system design,” 22nd International Conference on

Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2009), Tainan, Taiwan, June

24-27, 2009. Keynote Speech

[361] H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, “Single-objective and multi-objective formulations of solution

selection for hypervolume maximization,” Proc. of 2009 Genetic and Evolutionary Computation Conference, pp.

1831-1832, Montreal, Canada, July 8-12, 2009.

[362] H. Ishibuchi and Y. Nojima, “Discussions on interpretability of fuzzy systems using simple examples,” Proc. of 2009

IFSA World Congress and 2009 EUSFLAT Conference, pp. 1649-1654, Lisbon, Portugal, July 20-24, 2009.

[363] Y. Nojima and H. Ishibuchi, “Interactive fuzzy modeling by evolutionary multiobjective optimization with user

preference,” Proc. of 2009 IFSA World Congress and 2009 EUSFLAT Conference, pp. 1839-1844, Lisbon, Portugal, July

20-24, 2009.

[364] H. Ishibuchi and R. Alcala, “Evolutionary multiobjective optimization and fuzzy system design,” 2009 IEEE

International Conference on Fuzzy Systems, Jeju Island, Korea, August 20-24, 2009, Tutorial Talk.

[365] H. Ishibuchi, H. Ohyanagi, and Y. Nojima, “Evolution of cooperative behavior in a spatial Iterated Prisoner’s Dilemma

game with different representation schemes of game strategies,” Proc. of 2009 IEEE International Conference on Fuzzy

Systems, pp. 1568-1573, Jeju Island, Korea, August 20-24, 2009.

[366] H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, “Selecting a small number of representative non-dominated

solutions by a hypervolume-based solution selection approach,” Proc. of 2009 IEEE International Conference on Fuzzy

Systems, pp. 1609-1614, Jeju Island, Korea, August 20-24, 2009. Best Paper Award

[367] R. Alcala, Y. Nojima, F. Herrera, and H. Ishibuchi, “Generating single granularity-based fuzzy classification rules for

multiobjective genetic fuzzy rule selection,” Proc. of 2009 IEEE International Conference on Fuzzy Systems, pp.

1718-1723, Jeju Island, Korea, August 20-24, 2009.

[368] H. Ishibuchi, Y. Nakashima, and Y. Nojima, “Search ability of evolutionary multiobjective optimization algorithms for

multiobjective fuzzy genetics-based machine learning,” Proc. of 2009 IEEE International Conference on Fuzzy Systems,

pp. 1724-1729, Jeju Island, Korea, August 20-24, 2009.

[369] H. Ishibuchi, Y. Kaisho, and Y. Nojima, “Complexity, interpretability and explanation capability of fuzzy rule-based

classifiers,” Proc. of 2009 IEEE International Conference on Fuzzy Systems, pp. 1730-1735, Jeju Island, Korea, August

20-24, 2009.

[370] H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, “Evolutionary many-objective optimization by NSGA-II and

MOEA/D with large populations,” Proc. of 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp.

1820-1825, San Antonio, USA, October 10-13, 2009.

[371] H Ishibuchi, “Multiobjective genetic fuzzy systems - Accurate and interpretable fuzzy rule-based classifier design,”

Proc. of 9th International Conference on Intelligent Systems Design and Applications, pp. xli, Pisa, Italy, November

30-December 2, 2009. Invited Talk

[372] Y. Nojima and H Ishibuchi, “Effects of data reduction on the generalization ability of parallel distributed genetic fuzzy

rule selection,” Proc. of 9th International Conference on Intelligent Systems Design and Applications, pp. 96-101, Pisa,

Italy, November 30-December 2, 2009.

[373] Y. Nojima, Y. Nakashima, and H. Ishibuchi, “Effects of the user of multiple fuzzy partitions on the search ability of

multiobjective fuzzy genetics-based machine learning,” Proc. of International Conference on Soft Computing and Pattern

Recognition, pp. 341-346, Malacca, Malaysia, December 4-7, 2009.

[374] Y. Tsujimoto, Y. Hitotsuyanagi, Y. Nojima, and H. Ishibuchi, “Effects of including single-objective optimal solutions in

an initial population on evolutionary multiobjective optimization,” Proc. of International Conference on Soft Computing

and Pattern Recognition, pp. 352-357, Malacca, Malaysia, December 4-7, 2009.

[375] H. Ishibuchi, Y. Nakashima, and Y. Nojima, “Simple changes in problem formulations make a difference in

multiobjective genetic fuzzy systems,” Proc. of 4th International Workshop on Genetic and Evolutionary Fuzzy

Systems, pp. 3-8, Mieres, Spain, March 17-19, 2010.

[376] Y. Nojima, H. Ishibuchi, and S. Mihara, “Use of very small training data subsets in parallel distributed genetic fuzzy

rule selection,” Proc. of 4th International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 27-32, Mieres,

Spain, March 17-19, 2010.

[377] H. Ishibuchi, Y. Sakane, N. Tsukamoto and Y. Nojima, “Simultaneous use of different scalarizing functions in

MOEA/D,” Proc. of Genetic and Evolutionary Computation Conference - GECCO 2010, pp. 519-526, Portland, USA,

July 7-11, 2010.

[378] H. Ishibuchi, N. Tsukamoto, Y. Sakane and Y. Nojima, “Indicator-based evolutionary algorithm with hypervolume

approximation by achievement scalarizing functions,” Proc. of Genetic and Evolutionary Computation Conference -

GECCO 2010, pp. 527-534, Portland, USA, July 7-11, 2010.

[379] Y. Nojima, S. Mihara, and H. Ishibuchi, “Ensemble classifier design by parallel distributed implementation of genetic

fuzzy rule selection for large data sets,” Proc. of 2010 IEEE Congress on Evolutionary Computation, pp. 2113-2120,

Barcelona, Spain, July 18-23, 2010.

[380] Y. Nojima, Y. Kaisho, and H. Ishibuchi, “Accuracy implementation of genetic fuzzy rule selection with candidate rule

addition and membership tuning,” Proc. of 2010 IEEE International Conference on Fuzzy Systems, pp. 527-534,

Barcelona, Spain, July 18-23, 2010.

[381] H. Ishibuchi, Y. Nakashima, and Y. Nojima, “Effects of fine fuzzy partitions on the generalization ability of

evolutionary multi-objective fuzzy rule-based classifiers,” Proc. of 2010 IEEE International Conference on Fuzzy

Systems, pp. 1238-1245, Barcelona, Spain, July 18-23, 2010.

[382] H. Ishibuchi, “Memetic algorithms for evolutionary multiobjective combinatorial optimization,” 40th International

Conference on Computers and Industrial Engineering (CIE 40), Awajishima, Japan, July 25-28. 2010. Plenary Talk

[383] H. Ishibuchi, Y. Hitotsuyanagi, Y. Wakamatsu, and Y. Nojima, “How to choose solutions for local search in

multiobjective combinatorial memetic algorithms,” Proc. of 11th International Conference on Parallel Problem Solving

from Nature, Part I, pp. 516-525, Krakow, Poland, September 11-15, 2010.

[384] H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, “Many-objective test problems to visually examine the

behavior of multiobjective evolution in a decision space,” Proc. of 11th International Conference on Parallel Problem

Solving from Nature, Part II, pp. 91-100, Krakow, Poland, September 11-15, 2010.

[385] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Use of non-geometric binary crossover as mutation,” Proc. of World

Automation Congress - WAC 2010 (CD-ROM, 6 pages), Kobe, Japan, September 19-22, 2010. Best Paper Award

[386] H. Ishibuchi, “Evolutionary Design of Accurate and Interpretable Fuzzy Rule-Based Systems,” 2010 International

Workshop on Nature Inspired Computation and Applications, Hefei, China, October 23-27, 2010. Invited Talk

[387] H. Ishibuchi, “How to Combine Local Search with Evolutionary Algorithms for Multi-objective Combinatorial

Optimization,” 2010 International Workshop on Nature Inspired Computation and Applications, Hefei, China, October

23-27, 2010. Invited Talk

[388] Y. Nojima, S. Mihara and H. Ishibuchi, “Rotation effect of training data subsets in parallel distributed fuzzy

genetics-based machine learning,” Proc of 14th Asia Pacific Symposium on Intelligent and Evolutionary Systems,

(CD-ROM, 10 pages), Miyajima, Japan, November 19-20, 2010.

[389] H. Ishibuchi, “Recent trends in evolutionary multiobjective optimization research,” 14th Asia Pacific Symposium on

Intelligent and Evolutionary Systems, Miyajima, Japan, November 19-20, 2010. Keynote Speech

[390] Y. Nojima, S. Mihara, and H. Ishibuchi, “Parallel distributed implementation of genetics-based machine learning for

fuzzy classifier design,” Proc. of the 8th International Conference on Simulated Evolution and Learning - SEAL 2010, pp.

309-318, Kanpur, India, December 1-4, 2010..

[391] H. Ishibuchi, Y. Sakane, and Y. Nojima, “Use of multiple grids with different scalarizing functions in MOEA/D,” Proc.

of Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on

Advanced Intelligent Systems, pp. 898-903, Okayama, Japan, December 8-12, 2010. Best Paper Award

[392] H. Ishibuchi, Y. Hitotsuyanagi, Y. Nakashima, and Y. Nojima, “Multiobjectivization from two objectives to four

objectives in evolutionary multi-objective optimization algorithms,” Proc. of the World Congress on Nature and

Biologically Inspired Computing, pp. 509-514, Kitakyushu, Japan, December 15-17, 2010.

[393] S. Nishikawa, Y. Nojima, and H. Ishibuchi, “Appropriate granularity specification for fuzzy classifier design by data

complexity measures,” Proc. of the World Congress on Nature and Biologically Inspired Computing, pp. 698-703,

Kitakyushu, Japan, December 15-17, 2010.

[394] H. Ishibuchi, “Current hot issues and future challenges in EMO: From multiobjective to many-objective optimization,”

Computational Intelligence Winter School, Guangzhou, China, January 24-28, 2011. Invited Lecture

[395] H. Ishibuchi, “Fuzzy rule-based classifier design and fuzzy data mining: From heuristic rule extraction to multi-objective

optimization,” 2011 IEEE Computational Intelligence Society ExCom Seminar Series, Sao Paulo, Brazil, March 18, 2011.

Invited Lecture

[396] H. Ishibuchi, Y. Hitotsuyanagi, H. Ohyanagi, and Y.Nojima, “Effects of the Existence of Highly Correlated Objectives

on the Behavior of MOEA/D” Proc. of 6th International Conference on Evolutionary Multi-Criterion Optimization, pp.

166-181, Ouro Preto, Brazil, April 5-8, 2011.

[397] H. Ishibuchi, N. Akedo, H. Ohyanagi, Y. Hitotsuyanagi, and Y. Nojima, “Many-objective test problems with multiple

Pareto optimal regions in a decision space,” Proc. of 2011 IEEE Symposium on Computational Intelligence in

Multicriteria Decision-Making, pp. 113-120, Paris, France, April 11-15, 2011.

[398] H. Ishibuchi, Y. Nakashima, and Y. Nojima, “Double cross-validation for performance evaluation of multi-objective

genetic fuzzy systems,” Proc. of 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems, pp.

31-38, Paris, France, April 11-15, 2011.

[399] H. Ishibuchi, “Fuzzy rule-based classifier design: From heuristic rule extraction to multi-objective optimization,” 7th

Bi-Annual Statistics Congress of Turkish Statistical Association, Antalya, Turkey, May 30 (May 28 - April 1), 2011.

Invited Talk

[400] H. Ishibuchi, N. Akedo, H. Ohyanagi, and Y. Nojima, “Behavior of EMO algorithms on many-objective optimization

problems with correlated objectives,” Proc. of 2011 IEEE Congress on Evolutionary Computation, pp. 1465-1472, New

Orleans, USA, June 5-8, 2011

[401] Y. Nojima, S. Nishikawa, and H. Ishibuchi, “A meta-fuzzy classifier for specifying appropriate fuzzy partitions by

genetic fuzzy rule selection with data complexity measures,” Proc. of 2011 IEEE International Conference on Fuzzy

Systems, pp. 264-271, Taipei, Taiwan, June 27-30, 2011.

[402] H. Ishibuchi and Y. Nojima, “Toward quantitative definition of explanation ability of fuzzy rule-based classifiers,” Proc.

of 2011 IEEE International Conference on Fuzzy Systems, pp. 549-556, Taipei, Taiwan, June 27-30, 2011. Best Paper

Award

[403] H. Ishibuchi, N. Akedo, and Y. Nojima, “A many-objective test problem for visually examining diversity maintenance

behavior in a decision space,” Proc. of 2011 Genetic and Evolutionary Computation Conference - GECCO 2011, pp.

649-656, Dublin, Ireland, July 12-16, 2011.

[404] H. Ishibuchi, K. Takahashi, K. Hoshino, J. Maeda, and Y. Nojima, “Effects of configuration of agents with different

strategy representations on the evolution of cooperative behavior in a spatial IPD game,” Proc. of 2011 IEEE Conference

on Computational Intelligence and Games- CIG 2011, pp. 313-320, Seoul, Korea, August 31 - September 3, 2011.

[405] S. Mihara. Y. Nojima, and H. Ishibuchi, “Relation between Migration interval and data rotation interval in parallel

distributed fuzzy GBML,” Proc. of 12th International Symposium on Advanced Intelligent Systems, pp. 346-349, Suwon,

Korea, September 29 - October 1, 2011. Best Presentation Award

[406] Y. Nojima, S. Mihara, and H. Ishibuchi, “Parallel distributed genetic rule selection of association rules,” Abstract

Booklet of International Workshop on Simulation and Modeling related to Computational Science and Robotics

Technology, pp. 34-35, Kobe, Japan, November 1-3, 2011.

[407] Y. Nojima and H. Ishibuchi, “Mobile robot controller design by evolutionary multiobjective optimization in multiagent

environments,” Proc. of 4th International Conference on Intelligent Robotics and Applications, Part II, pp. 515-524,

Aachen, Germany, December 6-8, 2011.

[408] H. Ishibuchi, S. Mihara, and Y. Nojima, “Training data subdivision and periodical rotation in hybrid fuzzy

genetics-based machine learning,” Proc. of 10th International Conference on Machine Learning and Applications, pp.

229-234, Honolulu, Hawaii, USA, December 18-21, 2011.

[408] H. Ishibuchi, K. Hoshino, and Y. Nojima, “Strategy evolution in a spatial IPD game where each agent is not allowed to

play against itself,” Proc. of 2012 IEEE Congress on Evolutionary Computation, pp. 688-695, Brisbane, Australia, June,

10-15, 2012.

[409] H. Ishibuchi, K. Hoshino, and Y. Nojima, “Evolution of strategies in a spatial IPD Game with a number of different

representation schemes,” Proc. of 2012 IEEE Congress on Evolutionary Computation, pp. 808-815, Brisbane, Australia,

June, 10-15, 2012.

[410] Y. Nojima, S. Mihara, and H. Ishibuchi, “Application of parallel distributed genetics-based machine learning to

imbalanced data sets,” Proc. of 2012 IEEE International Conference on Fuzzy Systems, pp. 928-933, Brisbane, Australia,

June, 10-15, 2012.

[411] H. Ishibuchi, N. Akedo, and Y. Nojima, “EMO algorithms on correlated many-objective problems with different

correlation strength, ” Proc. of 2012 World Automation Congress, Puerto Vallarta, Mexico, June 24-27, 2012 (6 pages)

[412] H. Ishibuchi, M. Yamane, and Y. Nojima, “Effects of Discrete Objective Functions with Different Granularities on the

Search Behavior of EMO Algorithms,” Proc. of 2012 Genetic and Evolutionary Computation Conference - GECCO 2012,

pp. 481-488, Philadelphia, USA, July 7-11, 2012.

[413] H. Ishibuchi, “Fuzzy rule-based classifier design as understandable decision making systems,” 4th International

Conference on Intelligent Human-Machine Systems and Cybernetics - IHMSC 2012, Nanchang, China, August 26-27,

2012. Keynote Talk

[414] H. Ishibuchi, “Hot issues in evolutionary many-objective optimization,” 6th International Conference on Genetic and

Evolutionary Computing - ICGEC 2012, Kitakyushu, Japan, August 25-28, 2012. Keynote Talk

[415] H. Ishibuchi, N. Akedo, and Y. Nojima, “Recombination of similar parents in SMS-EMOA on many-objective 0/1

knapsack problems, ” Poc. of 12th International Conference on Parallel Problem Solving from Nature, Part II, pp.

132-142, Taormina, Italy, September 1-5, 2012.

[416] H. Ishibuchi, “Fuzzy rule-based classifier design,” 2012 Conference on Technologies and Applications of Artificial

Intelligence, Tainan, Taiwan, November 16-18, 2012. Keynote Talk

[417] M. Yamane, A. Ueda, N. Tadokoro, Y. Nojima, and H. Ishibuchi, “Comparison of different fitness functions in genetic

fuzzy rule selection,” Proc. of Joint 6th International Conference on Soft Computing and Intelligent Systems, and 13th

International Symposium on Advanced Intelligent Systems, pp. 1046-1051, Kobe, Japan, November 20-24, 2012.

[418] H. Ishibuchi, M. Yamane, N. Akedo, and Y. Nojima, “Two-objective solution set optimization to maximize

hypervolume and decision space diversity in multiobjective optimization,” Proc. of Joint 6th International Conference on

Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligent Systems, pp.

1871-1876, Kobe, Japan, November 20-24, 2012.

[419] H. Ishibuchi, K. Hoshino, and Y. Nojima, “Problem formulation of interactive evolutionary computation with minimum

requirement for human user’s fitness evaluation ability,” Proc. of 16th Asia Pacific Symposium on Intelligent and

Evolutionary Systems, pp. 52-57, Kyoto, Japan, December 12-14, 2012.

[420] H. Ishibuchi, M. Yamane, and Y. Nojima, “Ensemble fuzzy rule-based classifier design by parallel distributed fuzzy

GBML algorithms,” Proc. of 9th International Conference on Simulated Evolution and Learning - SEAL 2012, pp.

93-103, Hanoi, Vietnam, December 16-19, 2012.

[421] H. Ishibuchi, “Evolutionary multiobjective optimization and fuzzy rule-based classifier design,” 9th International

Conference on Simulated Evolution and Learning - SEAL 2012, Hanoi, Vietnam, December 16-19, 2012. Tutorial Talk

(December 16)

[422] H. Ishibuchi, “Fuzzy genetics-based machine learning,” 9th International Conference on Simulated Evolution and

Learning - SEAL 2012, Hanoi, Vietnam, December 16-19, 2012. Keynote Talk (December 17)

[423] H. Ishibuchi, “Parallel distributed fuzzy genetics-based machine learning,” 3rd International Conference on Swarm,

Evolutionary and Memetic Computing Conference (SEMCCO 2012) & Fuzzy and Neural Computing Conference

(FANCCO 2012), Bhubaneswar, India, December 20-22, 2012. Keynote Talk (December 21)

[424] H. Ishibuchi, N. Akedo, and Y. Nojima, “A study on the specification of a scalarizing function in MOEA/D for

many-objective knapsack problems,” Proc. of 7th International Conference on Learning and Intelligent Optimization, pp.

231-246, Catania, Italy, January 7-11, 2013.

[425] H. Ishibuchi, K. Hoshino, and Y. Nojima, “Neighborhood specification for game strategy evolution in a spatial iterated

prisoner’s dilemma game,” Proc. of 7th International Conference on Learning and Intelligent Optimization, pp. 215-230,

Catania, Italy, January 7-11, 2013.

[426] H. Ishibuchi, M. Yamane, and Y. Nojima, “Difficulty in evolutionary multiobjective optimization of discrete objective

functions with different granularities,” Proc. of 7th International Conference on Evolutionary Multi-Criterion

Optimization, pp. 230-245, Sheffield, UK, March 19-22, 2013.

[427] H. Ishibuchi, N. Akedo, and Y. Nojima, “Relation between neighborhood size and MOEA/D performance on

many-objective problems,” Proc. of 7th International Conference on Evolutionary Multi-Criterion Optimization, pp.

459-474, Sheffield, UK, March 19-22, 2013.

[428] H. Ishibuchi, M. Yamane, and Y. Nojima, “Effects of duplicated objectives in many-objective optimization problems on

the search behavior of hypervolume-based evolutionary algorithms,” Proc. of 2013 IEEE Symposium on Computational

Intelligence in Multi-Criteria Decision-Making, pp. 25-32, Singapore, April 16-19, 2013.

[429] M. Fazzolari, R. Alcalá, Y. Nojima, H. Ishibuchi, and F. Herrera, “Improving a fuzzy association rule-based

classification model by granularity learning based on heuristic measures over multiple granularities,” Proc. of 2013 IEEE

International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 44-51, Singapore, April 16-19, 2013.

[430] Y. Nojima and H. Ishibuchi, “Multiobjective genetic fuzzy rule selection with fuzzy relational rules,” Proc. of 2013

IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 60-67, Singapore, April 16-19, 2013.

[431] H. Ishibuchi, M. Yamane, N. Akedo, and Y. Nojima, “Many-objective and many-variable test problems for visual

examination of multiobjective search,” Proc. of 2013 IEEE Congress on Evolutionary Computation, pp. 1491-1498,

Cancún, México, June 20-23, 2013. [432] H. Ishibuchi, Y. Tanigaki, N. Akedo, and Y. Nojima, “How to strike a balance between local search and global search in

multiobjective memetic algorithms for multiobjective 0/1 knapsack problems,” Proc. of 2013 IEEE Congress on

Evolutionary Computation, pp. 1643-1650, Cancún, México, June 20-23, 2013. [433] H. Ishibuchi and Y. Nojima, “Difficulties in choosing a single final classifier from non-dominated solutions in

multiobjective fuzzy genetics-based machine learning,” Proc. of 2013 Joint IFSA World Congress NAFIPS Annual

Meeting (IFSA/NAFIPS), pp. 1203-1208, Edmonton, Canada, June 24-28, 2013.

[434] H. Ishibuchi, M. Yamane, and Y. Nojima, “Rule weight update in parallel distributed fuzzy genetics-based machine

learning with data rotation,” Proc. of 2013 IEEE International Conference on Fuzzy Systems, in CD-ROM (8 pages),

Hyderabad, India, July 10-15, 2013.

[435] H. Ishibuchi, “Evolutionary computation for single-objective, multi-objective and many-objective optimization,” IEEE

Workshop on Computational Intelligence: Theories, Applications and Future Directions, Indian Institute of Technology

Kanpur, India, July 14, 2013. Keynote Talk (July 14)

[436] H. Ishibuchi, T. Sudo, K. Hoshino, and Y. Nojima, “Evolution of cooperative strategies for iterated prisoner’s dilemma

on networks,” Proc. of Fifth International Conference on Computational Aspects of Social Networks (CASoN 2013), pp.

32-37, Fargo, USA, August 12-14, 2013.

[437] H. Ishibuchi, “Fuzzy rule-based classifier design: Accuracy maximization and complexity minimization,” 17th

International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2013),

Kitakyushu, Japan, September 9-11, 2013. Keynote Talk (September 10)

[438] H. Ishibuchi, M. Yamane, and Y. Nojima, “Learning from multiple data sets with different missing attributes and

privacy policies: Parallel distributed fuzzy genetics-based machine learning approach,” Proc. of IEEE Big Data 2013

Workshop on Scalable Machine Learning: Theory and Applications, pp. 63-70, Santa Clara, CA, USA, October 6-9,

2013.

[439] H. Ishibuchi, T. Sudo, K. Hoshino, and Y. Nojima, “Effects of the number of opponents on the evolution of cooperation

in the iterated prisoner’s dilemma,” Proc. of 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp.

2001-2006, Manchester, UK, October 13-16, 2013.

[440] Y. Nojima, P. Ivarsson, and H. Ishibuchi, “Application of parallel distributed implementation to GAssist and its

sensitivity analysis on the number of sub-populations and training data subsets,” Proc. of 14th International Symposium

on Advanced Intelligent Systems (USB 10 pages), Daejeon, Korea, November 13-16, 2013.

[441] Y. Nojima, Y. Takahashi, M. Yamane, and H. Ishibuchi, “Environmental selection schemes for rule removal in

Michigan-style fuzzy genetics-based machine learning,” Proc. of 14th International Symposium on Advanced Intelligent

Systems (USB 10 pages), Daejeon, Korea, November 13-16, 2013.

[442] H. Ishibuchi, M. Yamane, and Y. Nojima, “Evolutionary multiobjective design of fuzzy rule-based classifiers with

explanation ability,” Conference on Business Analytics in Finance and Industry (BAFI 2014), One-Page Abstract,

Santiago, Chili, January 6-9, 2014.

[443] T. Sudo, K. Ueba, Y. Nojima, and H. Ishibuchi, “Interactive (1+1) evolutionary strategy with one-fifth success rule,”

Proc. of 2nd Asia-Pacific Conference on Computer Aided System Engineering, pp. 294-300, Bali, Indonesia, February

10-12, 2014.

[444] H. Ishibuchi, T. Sudo, and Y. Nojima, “Archive management in interactive evolutionary computation with minimum

requirement for human user’s fitness evaluation ability,” Proc. of 13th International Conference on Artificial Intelligence

and Soft Computing, pp. 360-371, Zakopane, Poland, June 1-5, 2014.

[445] H. Ishibuchi, “Multi-objective evolutionary fuzzy rule-based classifier design,” International Conference on Artificial

Intelligence and Soft Computing, Zakopane, Poland, June 1-5, 2014. Invited Talk (June 3)

[446] Y. Takahashi, Y. Nojima, and H. Ishibuchi, “Hybrid fuzzy genetics-based machine learning with entropy-based

inhomogeneous interval discretization,” Proc. of 2014 IEEE International Conference on Fuzzy Systems, pp. 1512-1517,

Beijing, China, July 6-11, 2014.

[447] G. Acampora, H. Ishibuchi, and A. Vitiello, “A comparison of multi-objective evolutionary algorithms for the ontology

meta-matching problem,” Proc. of 2014 IEEE Congress on Evolutionary Computation, pp. 413-420, Beijing, China, July

6-11, 2014.

[448] H. Masuda, Y. Nojima, and H. Ishibuchi, “Visual examination of the behavior of EMO algorithms for many-objective

optimization with many decision variables,” Proc. of 2014 IEEE Congress on Evolutionary Computation, pp. 2633-2640,

Beijing, China, July 6-11, 2014.

[449] T. Sudo, Y. Nojima, and H. Ishibuchi, “Effects of ensemble action selection on the evolution of iterated prisoner’s

dilemma game strategies,” Proc. of 2014 IEEE Congress on Evolutionary Computation, pp. 1195-1201, Beijing, China,

July 6-11, 2014.

[450] K. Narukawa, Y. Tanigaki, and H. Ishibuchi, “Evolutionary many-objective optimization using preference on

hyperplane,” Companion of 2014 Genetic and Evolutionary Computation Conference, pp. 91-92, Vancouver, Canada,

July 12-16, 2014.

[451] H. Ishibuchi, H. Masuda, and Y. Nojima, “Meta-level multi-objective formulations of set optimization for

multi-objective optimization problems: Multi-reference point approach to hypervolume maximization,” Companion of

2014 Genetic and Evolutionary Computation Conference, pp. 89-90, Vancouver, Canada, July 12-16, 2014.

[452] H. Ishibuchi, Y. Tanigaki, H. Masuda, and Y. Nojima, “Distance-based analysis of crossover operators for

many-objective knapsack problems,” Proc. of 13th International Conference on Parallel Problem Solving from Nature,

pp. 600-610, Ljubljana, Slovenia, September 13-17, 2014.

[453] H. Ishibuchi, H. Masuda, and Y. Nojima, “Selecting a small number of non-dominated solutions to be presented to the

decision maker,” Proc. of 2014 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3850-3855, San

Diego, CA, USA, October 5-8, 2014.

[454] H. Ishibuchi, T. Sudo, K. Ueba, and Y. Nojima, “Offline design of interactive evolutionary algorithms with different

genetic operators at each generation,” Proc. of 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, vol.

2, pp. 635-646, Singapore, November 10-12, 2014.

[455] H. Ishibuchi, “Research topics in evolutionary many-objective optimization,” 18th Asia Pacific Symposium on

Intelligent and Evolutionary Systems, Singapore, November 10-12, 2014. Plenary Talk (November 10)

[456] Y. Tanigaki, K. Narukawa, Y. Nojima, and H. Ishibuchi, “Preference-based NSGA-II for many-objective knapsack

problems,” Proc. of 7th International Conference on Soft Computing and Intelligent Systems and 15th International

Symposium on Advanced Intelligent Systems, pp. 637-642, Kitakyushu, Japan, December 3-6, 2014.

[457] Y. Takahashi, Y. Nojima, and H. Ishibuchi, “Genetic lateral tuning of membership functions as post-processing for

hybrid fuzzy genetics-based machine learning,” Proc. of 7th International Conference on Soft Computing and Intelligent

Systems and 15th International Symposium on Advanced Intelligent Systems, pp. 667-672, Kitakyushu, Japan, December

3-6, 2014.

[458] H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, “Difficulties in specifying reference points to calculate the

inverted generational distance for many-objective optimization problems,” Proc. of 2014 IEEE Symposium on

Computational Intelligence in Multi-Criteria Decision-Making, pp. 170-177, Orlando, Florida, USA, December 9-12,

2014.

[459] H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, “Review of coevolutionary developments of evolutionary

multi-objective and many-objective algorithms and test problems,” Proc. of 2014 IEEE Symposium on Computational

Intelligence in Multi-Criteria Decision-Making, pp. 178-185, Orlando, Florida, USA, December 9-12, 2014.

[460] Y. Nojima, Y. Takahashi, and H. Ishibuchi, “Application of parallel distributed implementation to multiobjective fuzzy

genetics-based machine learning,” Proc. of 7th Asian Conference on Intelligent Information and Database Systems, Part I,

pp. 462-471, Bali, Indonesia, March 23-25, 2015.

[461] H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, “Modified distance calculation in generational distance and

inverted generational distance,” Proc. of 8th International Conference on Evolutionary Multi-Criterion Optimization, Part

I, pp. 110-125, Guimaraes, Portugal, March 29-April 1, 2015.

[462] Y. Setoguchi, K. Narukawa, and H. Ishibuchi, “A knee-based EMO algorithm with an efficient method to update mobile

reference points,” Proc. of 8th International Conference on Evolutionary Multi-Criterion Optimization, Part I, pp.

202-217, Guimaraes, Portugal, March 29-April 1, 2015.

[463] Y. Tanigaki, H. Masuda, Y. Setoguchi, Y. Nojima, and H. Ishibuchi, “Algorithm structure optimization by choosing

operators in multiobjective genetic local search,” Proc. of 2015 IEEE Congress on Evolutionary Computation, pp.

854-861, Sendai, Japan, May 25-28, 2015.

[464] T. Sudo, K. Goto, Y. Nojima, and H. Ishibuchi, “Effects of ensemble action selection with different usage of player’s

memory resource on the evolution of cooperative strategies for iterated prisoner’s dilemma game,” Proc. of 2015 IEEE

Congress on Evolutionary Computation, pp. 1505-1512, Sendai, Japan, May 25-28, 2015.

[465] H. Ishibuchi, H. Masuda, and Y. Nojima, “Comparing solution sets of different size in evolutionary many-objective

optimization,” Proc. of 2015 IEEE Congress on Evolutionary Computation, pp. 2859-2866, Sendai, Japan, May 25-28,

2015.

[466] Y. Nojima, K. Watanabe, and H. Ishibuchi, “Effects of heuristic rule generation from multiple patterns in multiobjective

fuzzy genetics-based machine learning,” Proc. of 2015 IEEE Congress on Evolutionary Computation, pp. 2996-3003,

Sendai, Japan, May 25-28, 2015.

[467] T. Sudo, K. Goto, Y. Nojima, and H. Ishibuchi, “Strange evolution behavior of 7-bit binary string strategies in iterated

prisoner’s dilemma game,” Proc. of 2015 IEEE Congress on Evolutionary Computation, pp. 3346-3353, Sendai, Japan,

May 25-28, 2015.

[468] Y. Takahashi, Y. Nojima, and H. Ishibuchi, “Rotation effects of objective functions in parallel distributed multiobjective

fuzzy genetics-based machine learning,” Proc. of 10th Asian Control Conference, 6 pages, Kota Kinabalu, Malaysia, May

31-June 3, 2015.

[469] H. Ishibuchi, “Evolutionary multi-objective optimization: Test problems and performance indicators,” IEEE CIS 2015

Summer School on Multi-Objective Optimization and Decision Making, Hefei, China, July 6-8, 2015. Invited Talk (July

6)

[470] H. Ishibuchi, “Evolutionary many-objective optimization: Difficulties and future research topics,” IEEE CIS 2015

Summer School on Multi-Objective Optimization and Decision Making, Hefei, China, July 6-8, 2015. Invited Talk (July

7)

[471] H. Ishibuchi, H. Masuda, and Y. Nojima, “A study on performance evaluation ability of a modified inverted

generational distance indicator,” Proc. of Genetic and Evolutionary Computation Conference, pp. 695-702, Madrid,

Spain, July 11-15, 2015.

[472] Y. Nojima, K. Watanabe, and H. Ishibuchi, “Simple modifications on heuristic rule generation and rule evaluation in

Michigan-style fuzzy genetics-based machine learning,” Proc. of 2015 IEEE International Conference on Fuzzy Systems,

8 pages, Istanbul, Turkey, August 2-5, 2015.

[473] H. Ishibuchi and Y. Nojima, “Handling a training dataset as a black-box model for privacy preserving in fuzzy GBML

algorithms,” Proc. of 2015 IEEE International Conference on Fuzzy Systems, 8 pages, Istanbul, Turkey, August 2-5,

2015.

[474] H. Ishibuchi, “Evolutionary many-objective optimization,” 4th International Conference on Frontiers in Intelligent

Computing: Theory and Applications, Durgapur, India, November 16-18, 2015. Keynote Talk (November 17)

[475] H. Ishibuchi, “Evolutionary many-objective optimization: Search behavior, performance indicators and test problems,”

IEEE CIS Distinguisher Lecturer Program at Kolkata Chapter, Kolkata, India, November 19, 2015. Invited Talk

(November 19)

[476] H. Ishibuchi, “Parallel distributed fuzzy system design,” 2015 IEEE International Conference on Research in

Computational Intelligence and Communication Networks, Kolkata, India, November 20-22, 2015. Keynote Talk

(November 20)

[477] Y. Nojima, K. Watanabe, and H. Ishibuchi, “Variants of heuristic rule generation from multiple patterns in

Michigan-style fuzzy genetics-based machine learning,” Proc. of 2015 Conference on Technologies and Applications of

Artificial Intelligence, pp. 427-432, Tainan, Taiwan, Nov. 20-22, 2015. Merit Paper Award

[478] H. Ishibuchi, K. Doi, H. Masuda, and Y. Nojima, “Relation between weight vectors and solutions in MOEA/D,” Proc.

of 2015 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, pp. 861-868, Cape Town,

December 8-10, 2015.

[479] B. Chen, R. Qu, R. Bai, and H. Ishibuchi, “A variable neighbourhood search algorithm with compound neighbourhoods

for VRPTW,” Proc. of 2016 International Conference on Operations Research and Enterprise Systems (ICORES'16),

Rome, Italy, February 23-25, 2016. (11 pages).

[480] H. Zille, H. Ishibuchi, S. Mostaghim, and Y. Nojima, “Weighted optimization framework for large-scale multi-objective

optimization,” Companion of 2016 Genetic and Evolutionary Computation Conference, pp. 83-84, Denver, USA, July

20-24, 2016.

[481] Y. Nojima and H. Ishibuchi, “Multiobjective fuzzy genetics-based machine learning with a reject option,” Proc. of 2016

IEEE International Conference on Fuzzy Systems, pp. 1405-1412, Vancouver, Canada, July 24-29, 2016.

[482] T. Sudo, K. Goto, Y. Nojima, and H. Ishibuchi, “Further analysis on strange evolution behavior of 7-bit binary string

strategies in iterated prisoner’s dilemma game,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 335-342,

Vancouver, Canada, July 24-29, 2016.

[483] H. Ishibuchi, H. Masuda, and Y. Nojima, “Sensitivity of performance evaluation results by inverted generational

distance to reference points,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 1107-1114, Vancouver,

Canada, July 24-29, 2016.

[484] H. Ishibuchi, K. Doi, and Y. Nojima, “Characteristics of many-objective test problems and penalty parameter

specification in MOEA/D,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 1115-1122, Vancouver,


[485] H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, “How to compare many-objective algorithms under different

settings of population and archive sizes,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 1149-1156,

Vancouver, Canada, July 24-29, 2016.

[486] Y. Nojima and H. Ishibuchi, “Effects of parallel distributed implementation on the search performance of

Pittsburgh-style genetics-based machine learning algorithms,” Proc. of 2016 IEEE Congress on Evolutionary

Computation, pp. 2193-2200, Vancouver, Canada, July 24-29, 2016.

[487] Y. Tanigaki, Y. Nojima, and H. Ishibuchi, “Meta-optimization based multi-objective test problem generation using

WFG toolkit,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 2768-2775, Vancouver, Canada, July

24-29, 2016.

[488] H. Masuda, Y. Nojima, and H. Ishibuchi, “Common properties of scalable multiobjective problems and a new

framework of test problems,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 3011-3018, Vancouver,


[489] H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Performance comparison of NSGA-II and NSGA-III on various

many-objective test problems,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 3045-3052, Vancouver,


[490] T. Funakoshi, Y. Nojima, and H. Ishibuchi, “Effects of different implementations of a real random number generator on

the search behavior of multiobjective evolutionary algorithms,” Proc. of Joint 8th International Conference on Soft

Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, pp. 172-177,

Sapporo, Hokkaido, August 26-28, 2016.

[491] H. Ishibuchi, K. Doi, and Y. Nojima, “Use of piecewise linear and nonlinear scalarizing functions in MOEA/D,” Proc.

of 14th International Conference on Parallel Problem Solving from Nature, pp. 503-523, Edinburgh, Scotland, UK,


[492] H. Ishibuchi, K. Doi, and Y. Nojima, “Reference point specification in MOEA/D for multi-objective and many-objective

problems,” Proc. of 2016 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4015-4020, Budapest,

Hungary, October 9-12, 2016.

[493] H. Ishibuchi, “Evolutionary multiobjective optimization,” 2016 International Workshop on Nature Inspired

Computation and Applications, Hefei, China, October 24-26, 2016. Invited Talk

[494] H. Ishibuchi, “Evolutionary many-objective optimization,” 2016 International Workshop on Nature Inspired

Computation and Applications, Hefei, China, October 24-26, 2016. Invited Talk

[495] H. Ishibuchi, K. Doi, and Y. Nojima, “Difficulties of MOEA/D with Tchebycheff function for many-objective DTLZ

1-4 problems,” Proc. of 7th International Symposium on Computational Intelligence and Industrial Applications, 6 pages,

Beijing, China, November 3-6, 2016.

[496] H. Ishibuchi, S. Takemura, and Y. Nojima, “Fitting and overfitting of multi-objective fuzzy genetics-based machine

learning to training data,” Proc. of 7th International Symposium on Computational Intelligence and Industrial

Applications, 6 pages, Beijing, China, November 3-6, 2016.

[497] H. Ishibuchi, “Evolutionary many-objective optimization: Difficulties and future research directions,” Applied

Informatics and Technology Innovation Conference (AITIC), Newcastle, Australia, November 22-24, 2016. Invited Talk

[498] H. Zille, H. Ishibuchi, S. Mostaghim, and Y. Nojima, “Mutation operators based on variable grouping for

multi-objective large-scale optimization,” Proc. of 2016 IEEE Symposium Series on Computational Intelligence (IEEE

SSCI 2016), 8 pages, Athens, Greece, December 6-9, 2016.

[499] R. Wang, H. Ishibuchi, Y. Zhang, X. Zheng, and T. Zhang, “On the effect of localized PBI method in MOEA/D for

multi-objective optimization,” Proc. of 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016),

8 pages, Athens, Greece, December 6-9, 2016.

[500] H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Hypervolume subset selection for triangular and inverted

triangular Pareto fronts of three-objective problems,” Proc. of 14th ACM/SIGEVO Conference on Foundations of Genetic

Algorithms (FOGA 2017), pp. 95-110, Copenhagen, Denmark, January 12-15, 2017.

[501] Y. Tanigaki, Y. Nojima, and H. Ishibuchi, “Performance comparison of EMO algorithms on test problems with different

search space shape,” Proc. of Joint 17th World Congress of International Fuzzy Systems Association and 9th

International Conference on Soft Computing and Intelligent Systems, 6 pages, Otsu, Japan, June 27-30, 2017. Student

Best Paper Award

[502] Y. Nojima, S. Takemura, K. Watanabe, and H. Ishibuchi, “Michigan-style fuzzy GBML with (1+1)-ES generation

update and multi-pattern rule generation,” Proc. of Joint 17th World Congress of International Fuzzy Systems Association

and 9th International Conference on Soft Computing and Intelligent Systems, 6 pages, Otsu, Japan, June 27-30, 2017.

[503] Y. Nojima, K. Arahari, S. Takemura, and H. Ishibuchi, “Multiobjective fuzzy genetics-based machine learning based on

MOEA/D with its modifications,” Proc. of 2017 IEEE International Conference on Fuzzy Systems, 6 pages, Naples, Italy,

July 9-12, 2017.

[504] H. B. Nguyen, B. Xue, H. Ishibuchi, P. Andreae, and M. Zhang, “Multiple reference points MOEA/D for feature

selection,” Companion of 2017 Genetic and Evolutionary Computation Conference, pp. 157-158, Berlin, Germany, July

15-19, 2017. [505] Y. Nojima, Y. Tanigaki, and H. Ishibuchi, “Multiobjective data mining from solutions by evolutionary multiobjective

optimization,” Proc. of 2017 Genetic and Evolutionary Computation Conference, pp. 617-624, Berlin, Germany, July 15-19, 2017.

[506] H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Reference point specification in hypervolume calculation for fair

comparison and efficient search,” Proc. of 2017 Genetic and Evolutionary Computation Conference, pp. 585-592, Berlin, Germany, July 15-19, 2017. Best Paper Award

[507] B. Chen, R. Qu, and H. Ishibuchi, “Variable-depth adaptive large neighbourhood search algorithm for open periodic vehicle routing problem with time windows,” Proc. of International Workshop on Harbour, Maritime & Multimodal Logistics Modelling and Simulation (HMS 2017), Barcelona, Spain, September 18-20, 2017. (11 pages)

[508] H. Ishibuchi, R. Imada, K. Doi, and Y. Nojima, “Use of inverted triangular weight vectors in decomposition-based multiobjective algorithms,” Proc. of 2017 IEEE International Conference on Systems, Man, and Cybernetics, pp. 373-378, Banff, Canada, October 5-8, 2017.

[509] H. Gao, Y. Nojima, and H. Ishibuchi, “Multi-objective GAssist with NSGA-II,” Proc. of 18th International Symposium on Advanced Intelligent Systems, pp. 696-703, Deagu, Republic of Korea, October 11-14, 2017.

[510] H. Ishibuchi, “Hot research topics in evolutionary many-objective optimization,” The 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII 2017) and The NSFC-CAS-JSPS Symposium “International Workshop on Frontier of Science and Technology 2017” (FST 2017), Beijing, China, Nov. 2-5, 2017. Keynote.

[511] K. Doi, R. Imada, Y. Nojima, and H. Ishibuchi, “Use of inverted triangular weight vectors in decomposition-based many-objective algorithms,” Proc. of 11th International Conference on Simulated Evolution and Learning, pp. 321-333, Shenzhen, China, Nov. 10-13, 2017.

[512] H. Ishibuchi, “Evolutionary Many-Objective Optimization and Performance Evaluation,” 11th International Conference on Simulated Evolution and Learning, Shenzhen, China, Nov. 10-13, 2017. Keynote.

Springer Lecture Notes Series [1] H. Ishibuchi, T. Nakashima, and T. Murata, “A fuzzy classifier system that generates linguistic rules for pattern

classification,” Lecture Notes in Artificial Intelligence 1152: Fuzzy Logic, Neural Networks, and Evolutionary Computation, pp. 35-54, Springer, Berlin, October 1996.

[2] T. Murata, H. Ishibuchi, T. Nakashima, and 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, San Diego, 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 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.

[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 and 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, and 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 and 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 and Y. Shibata, “A similarity-based mating scheme for evolutionary multiobjective optimization,” Lecture

Notes in Computer Science 2723: Genetic and Evolutionary Computation - GECCO 2003, pp. 1065-1076, Springer,

Berlin, July 2003.

[11] H. Ishibuchi and T. Yamamoto, “Evolutionary multiobjective optimization for generating an ensemble of fuzzy

rule-based classifiers,” Lecture Notes in Computer Science 2723: Genetic and Evolutionary Computation - GECCO 2003,

pp. 1077-1088, Springer, Berlin, July 2003.

[12] T. Murata, S. Kaige, and H. Ishibuchi, “Generalization of dominance relation-based replacement rules for memetic EMO

algorithms,” Lecture Notes in Computer Science 2723: Genetic and Evolutionary Computation - 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 genetics-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 and 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 and 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.


[17] T. Nakashima, H. Ishibuchi, and 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 and 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 and K. Narukawa, “Recombination of similar parents in EMO algorithms,” Lecture Notes in Computer

Science 3410: Evolutionary Multi-Criterion Optimization, pp. 265-279, Springer, Berlin, March 2005.

[20] Y. Nojima, K. Narukawa, S. Kaige, and 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, pp.

341-354, Springer, Berlin, March 2005.

[21] H. Ishibuchi, S. Kaige, and K. Narukawa, “Comparison between Lamarckian and Baldwinian repair on multiobjective 0/1

knapsack problems,” Lecture Notes in Computer Science 3410: Evolutionary Multi-Criterion Optimization, 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.


[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.


[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, October 2006.

[26] 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, March 2007.

[27] 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, September 2007.

[28] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Examining the effect of elitism in cellular genetic algorithms using two

neighborhood structures,” Lecture Notes in Computer Science 5199: Parallel Problem Solving from Nature - PPSN X, pp.


[29] H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, “Use of heuristic local search for single-objective

optimization in multiobjective memetic algorithms,” Lecture Notes in Computer Science 5199: Parallel Problem Solving

from Nature - PPSN X, pp. 743-752, Springer, Berlin, September 2008.

[30] Y. Nojima and H. Ishibuchi, “Effects of diversity measures on the design of ensemble classifiers by multiobjective

genetic fuzzy rule selection with a multi-classifier coding scheme,” Lecture Notes in Artificial Intelligence 5271: Hybrid

Artificial Intelligence Systems - HAIS 2008, pp. 755-762, Springer, Berlin, September 2008.

[31] E. -G. Talbi, S. Mostaghim, T. Okabe, H. Ishibuchi, G. Rudolph, and C. A. C. Coello, “Parallel approaches for

multiobjective optimization,” Lecture Notes in Artificial Intelligence 5252: Multiobjective Optimization: Interactive and

Evolutionary Approaches, pp. 349-372, Springer, Berlin, November 2008.

[32] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Use of local ranking in cellular genetic algorithms with two neighborhood

structures,” Lecture Notes in Computer Science 5361: Simulated Evolution and Learning (7th International Conference

on Simulated Evolution and Learning), pp. 309-318, Springer, Berlin, December 2008.

[33] H. Ishibuchi, Yuji Sakane, Noritake Tsukamoto, and Y. Nojima, “Adaptation of scalarizing functions in MOEA/D: An

adaptive scalarizing function-based multiobjective evolutionary algorithm,” Lecture Notes in Computer Science 5467:

Evolutionary Multi-Criterion Optimization - EMO 2009, pp. 438-452, Springer, Berlin, April 2009.

[34] H. Ishibuchi, Y. Hitotsuyanagi, Y. Wakamatsu, and Y. Nojima, “How to Choose Solutions for Local Search in

Multiobjective Combinatorial Memetic Algorithms,” Lecture Notes in Computer Science 6238: Parallel Problem Solving

from Nature - PPSN XI, part I, pp. 516-525, Springer, Berlin, September 2010.

[35] H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, “Many-Objective Test Problems to Visually Examine the

Behavior of Multiobjective Evolution in a Decision Space,” Lecture Notes in Computer Science 6239: Parallel Problem

Solving from Nature - PPSN XI, part II, pp. 91-100, Springer, Berlin, September 2010.

[36] Y. Nojima, S. Mihara, and H. Ishibuchi, “Parallel distributed implementation of genetics-based machine learning for

fuzzy classifier design,” Lecture Notes in Computer Science 6457: Simulated Evolution and Learning (8th International

Conference on Simulated Evolution and Learning), pp. 309-318, Springer, Berlin, December 1-4, 2010.

[37] H. Ishibuchi, Y. Hitotsuyanagi, H. Ohyanagi, and Y. Nojima, “Effects of the Existence of Highly Correlated Objectives

on the Behavior of MOEA/D,” Lecture Notes in Computer Science 6576: Evolutionary Multi-Criterion Optimization -

EMO 2011, pp. 166-181, Ouro Preto, Brazil, April 5-8, 2011.

[38] Y. Nojima and H. Ishibuchi, “Mobile robot controller design by evolutionary multiobjective optimization in multiagent

environments,” Lecture Notes in Artificial Intelligence 7102: Intelligent Robotics and Applications - ICIRA 2011, Part II,

pp. 515-524, Springer, Heidelberg, December 2011.

[39] H. Ishibuchi, N. Akedo, and Y. Nojima, “Recombination of similar parents in SMS-EMOA on many-objective 0/1

knapsack problems, ” Lecture Notes in Computer Science 7492: Parallel Problem Solving from Nature - PPSN XII, Part

II, pp. 132-142, Springer, Berlin, September 2012.

[40] H. Ishibuchi, M. Yamane, and Y. Nojima, “Ensemble fuzzy rule-based classifier design by parallel distributed fuzzy

GBML algorithms,” Lecture Notes in Computer Science 7673: Simulated Evolution and Learning (9th International

Conference on Simulated Evolution and Learning), pp. 93-103, Springer, Berlin, December 16-19, 2012.

[41] H. Ishibuchi, N. Akedo, and Y. Nojima, “A study on the specification of a scalarizing function in MOEA/D for

many-objective knapsack problems,” Lecture Notes in Computer Science Volume 7997: Learning and Intelligent

Optimization – LION 7, pp. 231-246, Springer, January 2013.

[42] H. Ishibuchi, K. Hoshino, and Y. Nojima, “Neighborhood specification for game strategy evolution in a spatial iterated

prisoner’s dilemma game,” Lecture Notes in Computer Science Volume 7997: Learning and Intelligent Optimization –

LION 7, pp. 215-230, Springer, January 2013.

[43] H. Ishibuchi, M. Yamane, and Y. Nojima, “Difficulty in evolutionary multiobjective optimization of discrete objective

functions with different granularities,” Lecture Notes in Computer Science 7811: Evolutionary Multi-Criterion

Optimization - EMO 2013, pp. 230-245, Springer, Berlin, March 2013.

[44] H. Ishibuchi, N. Akedo, and Y. Nojima, “Relation between neighborhood size and MOEA/D performance on

many-objective problems,” Lecture Notes in Computer Science 7811: Evolutionary Multi-Criterion Optimization - EMO

2013, pp. 459-474, Springer, Berlin, March 2013.

[45] H. Ishibuchi, T. Sudo, and Y. Nojima, “Archive management in interactive evolutionary computation with minimum

requirement for human user’s fitness evaluation ability,” Lecture Notes in Computer Science 8467: Artificial Intelligence

and Soft Computing – ICAISC 2014, pp. 360-371, June, 2014.

[46] H. Ishibuchi, Y. Tanigaki, H. Masuda, and Y. Nojima, “Distance-based analysis of crossover operators for

many-objective knapsack problems,” Lecture Notes in Computer Science 8672: Parallel Problem Solving from Nature –

PPSN XIII, pp. 600-610, Springer, Berlin, September 2014.

[47] Y. Nojima, Y. Takahashi, and H. Ishibuchi, “Application of parallel distributed implementation to multiobjective fuzzy

genetics-based machine learning,” Lecture Notes in Computer Science 9011: Intelligent Information and Database

Systems – ACIIDS 2015, Part I, pp. 462-471, Springer, March 2015.

[48] H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, “Modified distance calculation in generational distance and

inverted generational distance,” Lecture Notes in Computer Science 9018: Evolutionary Multi-Criterion Optimization –

EMO 2015, Part I, pp. 110-125, Springer, March 29-April 1, 2015.

[49] Y. Setoguchi, K. Narukawa, and H. Ishibuchi, “A knee-based EMO algorithm with an efficient method to update mobile

reference points,” Lecture Notes in Computer Science 9018: Evolutionary Multi-Criterion Optimization – EMO 2015,

Part I, pp. 202-217, Springer, March 29-April 1, 2015.

[50] H. Ishibuchi, K. Doi, and Y. Nojima, “Use of piecewise linear and nonlinear scalarizing functions in MOEA/D,” Lecture

Notes in Computer Science 9921: Parallel Problem Solving from Nature – PPSN XIV, pp. 503-523, Springer, Berlin,

September 2016.

[51] K. Doi, R. Imada, Y. Nojima, and H. Ishibuchi, “Use of inverted triangular weight vectors in decomposition-based

many-objective algorithms,” Lecture Notes in Computer Science 10593: Simulated Evolution and Learning – SEAL 2017,

pp. 321-333, Springer, November, 2017.

Other Articles [1] H. Ishibuchi, “Preface: 3rd International Conference on Fuzzy Logic, Neural Nets, and Soft Computing,” International

Journal of Approximate Reasoning, vol. 13, no. 4, pp. 247-248, November 1995. [2] H. Ishibuchi, Book Review of “Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases”,

Fuzzy Sets and Systems, vol. 141, no. 1, pp. 161-162, January 2004. [3] Y. S. Ong, N. Krasnogor, and H. Ishibuchi, “Special issue on memetic algorithms,” IEEE Trans. on Systems, Man, and

Cybernetics: Part B - Cybernetics, vol. 37, no. 1, pp. 2-5, February 2007. [4] Y. S. Ong, M. H. Lim, F. Neri, and H. Ishibuchi, “Special issue on emerging trends in soft computing: memetic

algorithms,” Soft Computing, vol. 13, no. 8-9, pp. 739-740, July 2009. [5] K. Tang, K. C. Tan, and H. Ishibuchi, “Guest editorial: Memetic algorithms for evolutionary multi-objective

optimization,” Memetic Computation, vol. 2, no. 1, p. 1, March 2010. [6] H. Ishibuchi, “IEEE CIS VP-Technical Activities Vision Statement,” Computational Intelligence Magazine, vol. 5, no. 2,

p. 6, May 2010 [7] Y. Nojima, R. Alcalá, H. Ishibuchi, and F. Herrera, “Special issue on evolutionary fuzzy systems,” Soft Computing, vol.

15, no. 12, pp. 2299-2301, November 2011. [8] R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, “Special issue on evolutionary fuzzy systems,” International Journal

of Computational Intelligence Systems, vol. 5, no. 2, pp. 209-211, April 2012. [9] R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, “Special Issue on Evolutionary Fuzzy Systems,” International J. of

Uncertainty Fuzziness and Knowledge-Based Systems, vol. 20, October 2012. [10] R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, “Special issue on “Evolutionary fuzzy systems” EFSs,”

Knowledge-Based Systems, vol. 54, pp. 1-2, December 2013. [11] H. Ishibuchi, “Message from the new Editor-in-Chief,” IEEE Computational Intelligence Magazine, vol. 9, no. 1, p. 2,

February 2014.

[12] H. Ishibuchi, “What is your main IEEE society,” IEEE Computational Intelligence Magazine, vol. 9, no. 2, p. 2, May 2014.

[13] H. Ishibuchi, “Big data era,” IEEE Computational Intelligence Magazine, vol. 9, no. 3, p. 2, August 2014. [14] H. Ishibuchi, “Geographical outreach activities,” IEEE Computational Intelligence Magazine, vol. 9, no. 4, p. 2,

November 2014. [15] H. Ishibuchi, “Top three news stories on IEEE CIM in 2014,” IEEE Computational Intelligence Magazine, vol. 10, no. 1,

p. 2, February 2015. [16] H. Ishibuchi, “Traveling with my laptop,” IEEE Computational Intelligence Magazine, vol. 10, no. 2, p. 2, May 2015. [17] H. Ishibuchi, “WCCI 2006 and WCCI 2016 in Vancouver,” IEEE Computational Intelligence Magazine, vol. 10, no. 3, p.

2, August 2015. [18] H. Ishibuchi, “Talking with Young Researchers,” IEEE Computational Intelligence Magazine, vol. 10, no. 4, p. 2,

November 2015. [19] H. Ishibuchi, “Second Term as Editor-in-Chief,” IEEE Computational Intelligence Magazine, vol. 11, no. 1, p. 2,

February 2016. [20] H. Ishibuchi, “CIS Distinguished Lecturers Program,” IEEE Computational Intelligence Magazine, vol. 11, no. 2, p. 2,

May 2016. [21] H. Ishibuchi, “Power of a Single Photo in the Big Data Era,” IEEE Computational Intelligence Magazine, vol. 11, no. 3, p.

2, August 2016. [22] H. Ishibuchi, “IEEE Standards,” IEEE Computational Intelligence Magazine, vol. 11, no. 4, p. 2, November 2016. [23] H. Ishibuchi, “New Journal, New Editor-in-Chief and New VP for Publications,” IEEE Computational Intelligence

Magazine, vol. 12, no. 1, p. 2, February 2017. [24] A. Serguieva, H. Ishibuchi, R. R. Yager, and V. P. Alade, “Special issue on fuzzy techniques in financial modeling and

simulation,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 2, pp. 245-248, April 2017. [25] H. Ishibuchi, “Smart World,” IEEE Computational Intelligence Magazine, vol. 12, no. 2, p. 2, May 2017. [26] H. Ishibuchi, “After 30 Years of Work in Osaka,” IEEE Computational Intelligence Magazine, vol. 12, no. 3, p. 2,

August 2017.

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