師資
教育背景
1992，大阪府立大學，博士
1985-1987，京都大學，碩士
1981-1985，京都大學，學士
工作經歷
2017至今，南方科技大學計算機科學與工程系講座教授
1999-2017，大阪府立大學教授
1994-1999，大阪府立大學副教授
1993年，大阪府立大學助理教授
1987-1993，大阪府立大學研究員
榮譽與獎項
2015 IEEE CIS杰出講師
2015 ACIIDS 2015最佳常規論文獎（印度尼西亞，國際會議）
2015 TAAI 2015優秀論文獎（臺南，臺灣，國際會議）
2015 IEEE Trans. on Cybernetics 杰出評論者
2014 美國電氣電子工程師學會會士 (IEEE Fellow)IEEE Fellow
2013 ISIS 2013最佳會議論文獎（韓國，國際會議）
2011 ISCI（系統，控制和信息研究所）最佳論文獎（日本）
2011年度FUZZ-IEEE 2011年度最佳論文獎（臺灣國際會議）
2011 SOFT（日本模糊理論與智能信息學會）貢獻獎（日本）
2010 WAC 2010最佳論文獎（日本，國際會議）
2010 SCIS＆ISIS 2010最佳論文獎（日本，國際會議）
2009年FUZZ-IEEE 2009年度最佳論文獎（韓國國際會議）
2009 IEEE Trans. on Fuzzy Systems優秀副主編2008（美國，IEEE CIS）
2007年GECCO 2007年一等獎（英國，國際會議）
2007年JSPS獎（日本，日本資助機構）
2006 HIS-NCEI 2006最佳論文獎（新西蘭，國際會議）
2006年SOFT（日本模糊理論與智能信息學會）杰出書獎（日本）
2005 ISIS 2005年度杰出論文獎（韓國，國際會議）
2004年SOFT（日本模糊理論與智能信息學會）貢獻獎（日本）
2004年GECCO 2004年最佳論文獎（美國國際會議）
1997年JIMA（日本工業管理協會）青年研究員獎（日本）
代表文章
[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.
其他信息
Google學術搜索：20,000篇引文，H-Index 63（2017年1月）
H-Index Researcher Ranking in Computer Science of Guide 2 Research：日本排名第2（2017年1月）
世界大學學術排名（上海Ranking）：高被引者（2016）
日本演化計算學會（JSEC）主席：2016-2018
JSEC期刊總編：2014-2018
IEEE計算智能雜志總編：2014-2017
IEEE計算智能學會副主席：2010-2013
日本模糊理論與智能信息學會副會長（SOFT）：2007-2009