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Yuki Tanigaki

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2020 – today

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2024
[j3]
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  - journals/jocss/ShigenakaTTWO24
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Shusuke Shigenaka, Shunki Takami, Yuki Tanigaki, Shuhei Watanabe, Masaki Onishi:
MAS-Bench: a benchmarking for parameter calibration of multi-agent crowd simulation. J. Comput. Soc. Sci. 7(2): 2121-2145 (2024)
[c16]
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  - conf/icann/HemmiTO24
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Kazuki Hemmi, Yuki Tanigaki, Masaki Onishi:
NAVIGATOR-D3: Neural Architecture Search Using VarIational Graph Auto-encoder Toward Optimal aRchitecture Design for Diverse Datasets. ICANN (1) 2024: 292-307
2022
[j2]
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Yoshihiko Ozaki, Yuki Tanigaki, Shuhei Watanabe, Masahiro Nomura, Masaki Onishi:
Multiobjective Tree-Structured Parzen Estimator. J. Artif. Intell. Res. 73: 1209-1250 (2022)
2021
[c15]
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Shusuke Shigenaka, Shunki Takami, Shuhei Watanabe, Yuki Tanigaki, Yoshihiko Ozaki, Masaki Onishi:
MAS-Bench: Parameter Optimization Benchmark for Multi-agent Crowd Simulation. AAMAS 2021: 1652-1654
2020
[c14]
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  - conf/cec/TanigakiOSO20
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Yuki Tanigaki, Yoshihiko Ozaki, Shusuke Shigenaka, Masaki Onishi:
Evolutionary Multiobjective Optimization for Pedestrian Route Guidance with Multiple Scenarios. CEC 2020: 1-7
[c13]
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Yoshihiko Ozaki, Yuki Tanigaki, Shuhei Watanabe, Masaki Onishi:
Multiobjective tree-structured parzen estimator for computationally expensive optimization problems. GECCO 2020: 533-541

2010 – 2019

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2018
[c12]
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  - conf/smc/MasuyamaTNI18
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Naoki Masuyama, Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization. SMC 2018: 745-750
2017
[c11]
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Yusuke Nojima, Yuki Tanigaki, Hisao Ishibuchi:
Multiobjective data mining from solutions by evolutionary multiobjective optimization. GECCO 2017: 617-624
[c10]
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Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Performance comparison of EMO algorithms on test problems with different search space shape. IFSA-SCIS 2017: 1-6
2016
[j1]
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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)
[c9]
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Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Meta-optimization based multi-objective test problem generation using WFG toolkit. CEC 2016: 2768-2775
2015
[c8]
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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
[c7]
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  - conf/emo/IshibuchiMTN15
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Hisao Ishibuchi, Hiroyuki Masuda, Yuki Tanigaki, Yusuke Nojima:
Modified Distance Calculation in Generational Distance and Inverted Generational Distance. EMO (2) 2015: 110-125
2014
[c6]
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  - conf/cimcdm/IshibuchiMTN14
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Hisao Ishibuchi, Hiroyuki Masuda, Yuki Tanigaki, Yusuke Nojima:
Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems. MCDM 2014: 170-177
[c5]
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Hisao Ishibuchi, Hiroyuki Masuda, Yuki Tanigaki, Yusuke Nojima:
Review of coevolutionary developments of evolutionary multi-objective and many-objective algorithms and test problems. MCDM 2014: 178-184
[c4]
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  - conf/gecco/NarukawaTI14
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Kaname Narukawa, Yuki Tanigaki, Hisao Ishibuchi:
Evolutionary many-objective optimization using preference on hyperplane. GECCO (Companion) 2014: 91-92
[c3]
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  - conf/ppsn/IshibuchiTMN14
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Hisao Ishibuchi, Yuki Tanigaki, Hiroyuki Masuda, Yusuke Nojima:
Distance-Based Analysis of Crossover Operators for Many-Objective Knapsack Problems. PPSN 2014: 600-610
[c2]
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Yuki Tanigaki, Kaname Narukawa, Yusuke Nojima, Hisao Ishibuchi:
Preference-based NSGA-II for many-objective knapsack problems. SCIS&ISIS 2014: 637-642
2013
[c1]
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Hisao Ishibuchi, Yuki Tanigaki, Naoya Akedo, Yusuke Nojima:
How to strike a balance between local search and global search in multiobjective memetic algorithms for multiobjective 0/1 knapsack problems. IEEE Congress on Evolutionary Computation 2013: 1643-1650

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