Hisao Ishibuchi 讲座教授 智能科学与工程

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Hisao Ishibuchi

讲座教授

智能科学与工程创新中心

E-mail: hisao@sustc.edu.cn


研究方向

计算智能,演化多目标优化,演化机器学习,模糊系统,语言数据挖掘,演化游戏


教育背景

1992,大阪府立大学,博士

1985-1987,京都大学,硕士

1981-1985,京都大学,学士


工作经历

2017至今,南方科技大学计算机科学与工程系讲座教授

1999-2017,大阪府立大学教授

1994-1999,大阪府立大学副教授

1993年,大阪府立大学助理教授

1987-1993,大阪府立大学研究员


荣誉与奖项

2015 IEEE CIS杰出讲师

2015 ACIIDS 2015最佳常规论文奖(印度尼西亚,国际会议)

2015 TAAI 2015优秀论文奖(台南,台湾,国际会议)

2015 IEEE Trans. on Cybernetics 杰出评论者

2014 美国电气电子工程师学会会士 (IEEE Fellow)IEEE Fellow

2013 ISIS 2013最佳会议论文奖(韩国,国际会议)

2011 ISCI(系统,控制和信息研究所)最佳论文奖(日本)

2011年度FUZZ-IEEE 2011年度最佳论文奖(台湾国际会议)

2011 SOFT(日本模糊理论与智能信息学会)贡献奖(日本)

2010 WAC 2010最佳论文奖(日本,国际会议)

2010 SCIS&ISIS 2010最佳论文奖(日本,国际会议)

2009年FUZZ-IEEE 2009年度最佳论文奖(韩国国际会议)

2009 IEEE Trans. on Fuzzy Systems优秀副主编2008(美国,IEEE CIS)

2007年GECCO 2007年一等奖(英国,国际会议)

2007年JSPS奖(日本,日本资助机构)

2006 HIS-NCEI 2006最佳论文奖(新西兰,国际会议)

2006年SOFT(日本模糊理论与智能信息学会)杰出书奖(日本)

2005 ISIS 2005年度杰出论文奖(韩国,国际会议)

2004年SOFT(日本模糊理论与智能信息学会)贡献奖(日本)

2004年GECCO 2004年最佳论文奖(美国国际会议)

1997年JIMA(日本工业管理协会)青年研究员奖(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.