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Yu Setoguchi

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affiliation: Osaka Prefecture University, Sakai, Japan

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2010 – 2019

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2018
[j4]
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Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, Yusuke Nojima:
How to Specify a Reference Point in Hypervolume Calculation for Fair Performance Comparison. Evol. Comput. 26(3) (2018)
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Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, Yusuke Nojima:
Reference Point Specification in Inverted Generational Distance for Triangular Linear Pareto Front. IEEE Trans. Evol. Comput. 22(6): 961-975 (2018)
2017
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Hisao Ishibuchi, Yu Setoguchi, Hiroyuki Masuda, Yusuke Nojima:
Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes. IEEE Trans. Evol. Comput. 21(2): 169-190 (2017)
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Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, Yusuke Nojima:
Hypervolume Subset Selection for Triangular and Inverted Triangular Pareto Fronts of Three-Objective Problems. FOGA 2017: 95-110
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Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, Yusuke Nojima:
Reference point specification in hypervolume calculation for fair comparison and efficient search. GECCO 2017: 585-592
2016
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Kaname Narukawa, Yu Setoguchi, Yuki Tanigaki, Markus Olhofer, Bernhard Sendhoff, Hisao Ishibuchi:
Preference representation using Gaussian functions on a hyperplane in evolutionary multi-objective optimization. Soft Comput. 20(7): 2733-2757 (2016)
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Hisao Ishibuchi, Yu Setoguchi, Hiroyuki Masuda, Yusuke Nojima:
How to compare many-objective algorithms under different settings of population and archive sizes. CEC 2016: 1149-1156
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Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, Yusuke Nojima:
Performance comparison of NSGA-II and NSGA-III on various many-objective test problems. CEC 2016: 3045-3052
2015
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Yuki Tanigaki, Hiroyuki Masuda, Yu Setoguchi, Yusuke Nojima, Hisao Ishibuchi:
Algorithm structure optimization by choosing operators in multiobjective genetic local search. CEC 2015: 854-861
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Yu Setoguchi, Kaname Narukawa, Hisao Ishibuchi:
A Knee-Based EMO Algorithm with an Efficient Method to Update Mobile Reference Points. EMO (1) 2015: 202-217

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