Faculty

ISHIBUCHI Hisao
Chair Professor

Educational Background
1992: Osaka Prefecture University, Ph D
1985-1987: Kyoto University, MS
1981-1985: Kyoto University, BS

Professional Experience
2017-: Chair Professor of Computer Science and Engineering Department, SUSTech
1999-2017: Professor, Osaka Prefecture University
1994-1999: Associate Professor, Osaka Prefecture University
1993: Assistant Professor, Osaka Prefecture University
1987-1993: Research Associate, Osaka Prefecture University

Honors & Awards
2015 IEEE CIS Distinguished Lecturer
2015 ACIIDS 2015 Best Regular Paper Award (Indonesia, International Conference)
2015 TAAI 2015 Merit Paper Award (Tainan, Taiwan, International Conference)
2015 IEEE Trans. on Cybernetics Outstanding reviewer
2014 IEEE Fellow
2013 ISIS 2013 Best Session Paper Award (Korea, International Conference)
2011 ISCI (Institute of Systems, Control and Information) Best Paper Award (Japan, Domestic Society)
2011 FUZZ-IEEE 2011 Best Paper award (Taiwan, International Conference)
2011 SOFT (Japan Society for Fuzzy Theory and Intelligent Informatics) Contribution Award (Japan, Domestic Society)
2010 WAC 2010 Best Paper Award (Japan, International Conference)
2010 SCIS & ISIS 2010 Best Paper Award (Japan, International Conference)
2009 FUZZ-IEEE 2009 Best Paper Award (Korea, International Conference)
2009 IEEE Trans. on Fuzzy Systems Outstanding Associate Editor 2008 (USA, IEEE CIS)
2007 GECCO 2007 Competition First Prize (UK, International Conference)
2007 JSPS Prize (Japan, Japanese Funding Agency)
2006 HIS-NCEI 2006 Best Paper Award (New Zealand, International Conference)
2006 SOFT (Japan Society for Fuzzy Theory and Intelligent Informatics) Outstanding Book Award (Japan, Domestic Society)
2005 ISIS 2005 Outstanding Paper Award (Korea, International Conference)
2004 SOFT (Japan Society for Fuzzy Theory and Intelligent Informatics) Contribution Award (Japan, Domestic Society)
2004 GECCO 2004 Best Paper Award (USA, International Conference)
1997 JIMA (Japan Industrial Management Association) Young Researcher Award (Japan, Domestic Society)

Selected Publication
[1] 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 (Online Available)
[2] 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 (Online Available)
[3] R. Wang, Z. Zhou, H. Ishibuchi, T. Liao, and T. Zhang, “Localized weighted sum method for many-objective optimization,” IEEE Trans. on Evolutionary Computation (Online Available).
[4] 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.
[5] 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.
[6] H. Ishibuchi, T. Sudo, and Y. Nojima, “Interactive evolutionary computation with minimum fitness evaluation requirement and offline algorithm design,” SpringerPlus, vol. 5, Paper No. 192, February 2016.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[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, 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.
[25] 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.

Others
Google Scholar: 20,000 Citations, H-Index 63 (January 2017)
H-Index Researcher Ranking in Computer Science of Guide 2 Research: Rank 2nd in Japan (January 2017)
Academic Ranking of World University (Shanghai Ranking): Most Cited Researchers (2016)
President of Japanese Society for Evolutionary Computation (JSEC): 2016-2018
Editor-in-Chief of JSEC Journal: 2014-2018
Editor-in-Chief of IEEE Computational Intelligence Magazine: 2014-2017
Vice President of IEEE Computational Intelligence Society: 2010-2013
Vice President of Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT): 2007-2009