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ORCIDYusuke Nojima
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- affiliation: Osaka Metropolitan University, Sakai, Japan
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2020 – today
- 2024
[j62]Naoki Masuyama
, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota:
Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory. IEEE Access 12: 139692-139710 (2024)- [j61]Erhao Zhou, Chi-Man Vong, Yusuke Nojima, Shitong Wang:
Internally and Generatively Decorrelated Ensemble of First-Order Takagi-Sugeno-Kang Fuzzy Regressors With Quintuply Diversity Guarantee. IEEE Trans. Fuzzy Syst. 32(3): 1288-1302 (2024) - [c201]Takato Kinoshita, Naoki Masuyama, Yusuke Nojima:
A Federated Data-driven Multiobjective Evolutionary Algorithm via Continual Learnable Clustering. CEC 2024: 1-7 - [c200]Rowan Fuerst, Naoki Masuyama, Yusuke Nojima:
Hierarchical Fuzzy Classifier Design Using a Reject Option. FUZZ 2024: 1-7 - [c199]Takeru Konishi, Naoki Masuyama, Jorge Casillas, Yusuke Nojima:
Fairness-aware Classifier Design via Multi-objective Fuzzy Genetics-based Machine Learning. FUZZ 2024: 1-8 - [c198]Chang-Shing Lee, Mei-Hui Wang, Jun-Kui Chiang, Naoyuki Kubota, Eri Sato-Shimokawara, Yusuke Nojima, Giovanni Acampora, Pei-Yu Wu, Szu-Chi Chiu, Sheng-Chi Yang, Chyan Zheng Siow:
Quantum Computational Intelligence with Generative AI Image for Human-Machine Interaction. FUZZ 2024: 1-8 - [c197]Yusuke Nojima, Shion Takasaki, Shinji Fukuda, Naoki Masuyama:
Importance of Temporal Information in Fish Habitat Assessment Using Multiobjective Fuzzy Genetics-Based Machine Learning. iFUZZY 2024: 1-6 - [c196]Kazuki Tashiro, Naoki Masuyama, Yusuke Nojima:
A Growing Hierarchical Clustering Algorithm via Parameter-free Adaptive Resonance Theory. IJCNN 2024: 1-6 - [i13]Eric Michael Vernon, Naoki Masuyama, Yusuke Nojima:
Integrating White and Black Box Techniques for Interpretable Machine Learning. CoRR abs/2407.08973 (2024) - 2023
- [j60]Takato Kinoshita, Naoki Masuyama, Yiping Liu, Yusuke Nojima, Hisao Ishibuchi:
Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-Based Clustering for Many-Objective Optimization. IEEE Access 11: 126066-126086 (2023) - [j59]Ying Xu, Huan Zhang, Lei Huang, Rong Qu, Yusuke Nojima:
A Pareto Front grid guided multi-objective evolutionary algorithm. Appl. Soft Comput. 136: 110095 (2023) - [j58]Yong Wang, Yuting Wang, Yuyan Han, Junqing Li, Kaizhou Gao, Yusuke Nojima:
Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm. Complex Syst. Model. Simul. 3(4): 282-306 (2023) - [j57]Zekang Bian, Jin Zhang, Yusuke Nojima, Fu-Lai Chung, Shitong Wang:
Hybrid-ensemble-based interpretable TSK fuzzy classifier for imbalanced data. Inf. Fusion 98: 101845 (2023) - [j56]Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi:
Multi-Label Classification via Adaptive Resonance Theory-Based Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 45(7): 8696-8712 (2023) - [j55]Mohamed Arezki Mellal, Enrico Zio, Sameer Al-Dahidi, Naoki Masuyama, Yusuke Nojima:
System design optimization with mixed subsystems failure dependencies. Reliab. Eng. Syst. Saf. 231: 109005 (2023) - [j54]Ying Xu, Chong Xu, Huan Zhang, Lei Huang, Yiping Liu, Yusuke Nojima, Xiangxiang Zeng:
A Multi-Population Multi-Objective Evolutionary Algorithm Based on the Contribution of Decision Variables to Objectives for Large-Scale Multi/Many-Objective Optimization. IEEE Trans. Cybern. 53(11): 6998-7007 (2023) - [j53]Erhao Zhou, Chi-Man Vong, Yusuke Nojima, Shitong Wang:
A Fully Interpretable First-Order TSK Fuzzy System and Its Training With Negative Entropic and Rule-Stability-Based Regularization. IEEE Trans. Fuzzy Syst. 31(7): 2305-2319 (2023) - [c195]Chang-Shing Lee, Mei-Hui Wang, Chih-Yu Chen, Marek Z. Reformat, Yusuke Nojima, Naoyuki Kubota:
Knowledge Graph-Based Genetic Fuzzy Agent for Human Intelligence and Machine Co-Learning. FUZZ 2023: 1-6 - [c194]Yusuke Nojima, Koyo Kawano, Hajime Shimahara, Eric Vernon, Naoki Masuyama, Hisao Ishibuchi:
Fuzzy Classifiers with a Two-Stage Reject Option. FUZZ 2023: 1-6 - [c193]Yusuke Nojima, Yuto Fujii, Naoki Masuyama, Yiping Liu, Hisao Ishibuchi:
A Decomposition-based Multi-modal Multi-objective Evolutionary Algorithm with Problem Transformation into Two-objective Subproblems. GECCO Companion 2023: 399-402 - [p8]Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tusar:
Identifying Properties of Real-World Optimisation Problems Through a Questionnaire. Many-Criteria Optimization and Decision Analysis 2023: 59-80 - [i12]Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi, Stefan Wermter:
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning. CoRR abs/2305.01507 (2023) - [i11]Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota:
Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory. CoRR abs/2309.03487 (2023) - 2022
- [j52]Naoki Masuyama, Narito Amako, Yuna Yamada, Yusuke Nojima, Hisao Ishibuchi:
Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning. IEEE Access 10: 68042-68056 (2022) - [j51]Bin Qin, Fulai Chung, Yusuke Nojima, Hisao Ishibuchi, Shitong Wang:
Fuzzy rule dropout with dynamic compensation for wide learning algorithm of TSK fuzzy classifier. Appl. Soft Comput. 127: 109410 (2022) - [j50]Ying Xu, Huan Zhang, Xiangxiang Zeng, Yusuke Nojima:
An adaptive convergence enhanced evolutionary algorithm for many-objective optimization problems. Swarm Evol. Comput. 75: 101180 (2022) - [j49]Xiongtao Zhang, Yusuke Nojima, Hisao Ishibuchi, Wenjun Hu, Shitong Wang:
Prediction by Fuzzy Clustering and KNN on Validation Data With Parallel Ensemble of Interpretable TSK Fuzzy Classifiers. IEEE Trans. Syst. Man Cybern. Syst. 52(1): 400-414 (2022) - [c192]Takato Kinoshita, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
Analytical Methods to Separately Evaluate Convergence and Diversity for Multi-objective Optimization. MIC 2022: 172-186 - [c191]Yuichi Omozaki, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
Evolutionary Multi-Objective Multi-Tasking for Fuzzy Genetics-Based Machine Learning in Multi-Label Classification. FUZZ-IEEE 2022: 1-8 - [c190]Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi, Zongying Liu:
Adaptive Resonance Theory-based Clustering for Handling Mixed Data. IJCNN 2022: 1-8 - [c189]Takato Kinoshita, Naoki Masuyama, Yusuke Nojima:
Search Process Analysis of Multiobjective Evolutionary Algorithms using Convergence-Diversity Diagram. SCIS/ISIS 2022: 1-6 - [c188]Takeru Konishi, Naoki Masuyama, Yusuke Nojima:
Effects of Accuracy-based Single-Objective Optimization in Multiobjective Fuzzy Genetics-based Machine Learning. SCIS/ISIS 2022: 1-6 - [c187]Maaya Yano, Naoki Masuyama, Yusuke Nojima:
Behavior Analysis of Constrained Multiobjective Evolutionary Algorithms using Scalable Constrained Multi-Modal Distance Minimization Problems. WAC 2022: 174-179 - [c186]Eric Michael Vernon, Naoki Masuyama, Yusuke Nojima:
Error-Reject Tradeoff Analysis on Two-Stage Classifier Design with a Reject Option. WAC 2022: 312-317 - [i10]Naoki Masuyama, Narito Amako, Yuna Yamada, Yusuke Nojima, Hisao Ishibuchi:
Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning. CoRR abs/2201.10713 (2022) - [i9]Naoki Masuyama, Itsuki Tsubota, Yusuke Nojima, Hisao Ishibuchi:
Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering. CoRR abs/2203.09879 (2022) - [i8]Takato Kinoshita, Naoki Masuyama, Yiping Liu, Yusuke Nojima, Hisao Ishibuchi:
Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization. CoRR abs/2204.10756 (2022) - 2021
- [j48]Xue Han, Yuyan Han, Qingda Chen, Junqing Li, Hongyan Sang, Yiping Liu, Quanke Pan, Yusuke Nojima:
Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm. Complex Syst. Model. Simul. 1(3): 198-217 (2021) - [j47]Suhang Gu, Yusuke Nojima, Hisao Ishibuchi, Shitong Wang:
Fuzzy Style K-Plane Clustering. IEEE Trans. Fuzzy Syst. 29(6): 1518-1532 (2021) - [j46]Bin Qin, Yusuke Nojima, Hisao Ishibuchi, Shitong Wang:
Realizing Deep High-Order TSK Fuzzy Classifier by Ensembling Interpretable Zero-Order TSK Fuzzy Subclassifiers. IEEE Trans. Fuzzy Syst. 29(11): 3441-3455 (2021) - [c185]Yiping Liu, Liting Xu, Yuyan Han, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi, Gary G. Yen:
Multi-Modal Multi-Objective Traveling Salesman Problem and its Evolutionary Optimizer. SMC 2021: 770-777 - [c184]Victor Villin, Naoki Masuyama, Yusuke Nojima:
Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation. SSCI 2021: 1-8 - [i7]Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi:
Multi-label Classification via Adaptive Resonance Theory-based Clustering. CoRR abs/2103.01511 (2021) - [i6]Victor Villin, Naoki Masuyama, Yusuke Nojima:
Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation. CoRR abs/2110.08122 (2021) - [i5]Chang-Shing Lee, Mei-Hui Wang, Yusuke Nojima, Marek Z. Reformat, Leo Guo:
AI-Fuzzy Markup Language with Computational Intelligence for High-School Student Learning. CoRR abs/2112.01228 (2021) - 2020
- [j45]Chun-Hao Chen, Hsiang Chou, Tzung-Pei Hong, Yusuke Nojima:
Cluster-Based Membership Function Acquisition Approaches for Mining Fuzzy Temporal Association Rules. IEEE Access 8: 123996-124006 (2020) - [j44]Ying Xu, Cuijuan Yang, Shaoliang Peng, Yusuke Nojima:
A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning. Appl. Intell. 50(11): 3852-3867 (2020) - [j43]Irene Díaz, Yusuke Nojima:
Fuzzy sets for decision making in emerging domains. Fuzzy Sets Syst. 395: 197-198 (2020) - [j42]Yiping Liu, Hisao Ishibuchi, Naoki Masuyama, Yusuke Nojima:
Adapting Reference Vectors and Scalarizing Functions by Growing Neural Gas to Handle Irregular Pareto Fronts. IEEE Trans. Evol. Comput. 24(3): 439-453 (2020) - [j41]Yiping Liu, Hisao Ishibuchi, Gary G. Yen, Yusuke Nojima, Naoki Masuyama:
Handling Imbalance Between Convergence and Diversity in the Decision Space in Evolutionary Multimodal Multiobjective Optimization. IEEE Trans. Evol. Comput. 24(3): 551-565 (2020) - [j40]Suhang Gu, Yusuke Nojima, Hisao Ishibuchi, Shitong Wang:
A Novel Classification Method From the Perspective of Fuzzy Social Networks Based on Physical and Implicit Style Features of Data. IEEE Trans. Fuzzy Syst. 28(2): 361-375 (2020) - [c183]Yuna Yamada, Naoki Masuyama, Narito Amako, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi:
Divisive Hierarchical Clustering Based on Adaptive Resonance Theory. CcS 2020: 1-6 - [c182]Ryuichi Hashimoto, Toshiki Urita, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
Effects of Local Mating in Inter-task Crossover on the Performance of Decomposition-based Evolutionary Multiobjective Multitask optimization Algorithms. CEC 2020: 1-8 - [c181]Yiping Liu, Hisao Ishibuchi, Gary G. Yen, Yusuke Nojima, Naoki Masuyama, Yuyan Han:
On the Normalization in Evolutionary Multi-Modal Multi-Objective Optimization. CEC 2020: 1-8 - [c180]Hisao Ishibuchi, Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima:
Many-Objective Problems Are Not Always Difficult for Pareto Dominance-Based Evolutionary Algorithms. ECAI 2020: 291-298 - [c179]Yuichi Omozaki, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
Multiobjective Fuzzy Genetics-Based Machine Learning for Multi-Label Classification. FUZZ-IEEE 2020: 1-8 - [c178]Koen van der Blom, Timo M. Deist, Tea Tusar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks:
Towards realistic optimization benchmarks: a questionnaire on the properties of real-world problems. GECCO Companion 2020: 293-294 - [c177]Hisao Ishibuchi, Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima:
Effects of dominance resistant solutions on the performance of evolutionary multi-objective and many-objective algorithms. GECCO 2020: 507-515 - [c176]Narito Amako, Naoki Masuyama, Chu Kiong Loo, Yusuke Nojima, Yiping Liu, Hisao Ishibuchi:
Multilayer Clustering Based on Adaptive Resonance Theory for Noisy Environments. IJCNN 2020: 1-8 - [c175]Ying Xu, Lymeng Chhim, Bingxin Zheng, Yusuke Nojima:
Stacked Deep Learning Structure with Bidirectional Long-Short Term Memory for Stock Market Prediction. NCAA 2020: 447-460 - [c174]Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi:
Multi-label Classification Based on Adaptive Resonance Theory. SSCI 2020: 1913-1920 - [i4]Koen van der Blom, Timo M. Deist, Tea Tusar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks:
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems. CoRR abs/2004.06395 (2020) - [i3]Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tusar:
Identifying Properties of Real-World Optimisation Problems through a Questionnaire. CoRR abs/2011.05547 (2020)
2010 – 2019
- 2019
- [j39]Naoki Masuyama, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota, Yusuke Nojima, Yiping Liu:
Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning. IEEE Access 7: 76920-76936 (2019) - [j38]Zhaohong Deng, Jie Lu, Dongrui Wu, Kup-Sze Choi, Shiliang Sun, Yusuke Nojima:
Guest Editorial: Special Issue on New Advances in Deep-Transfer Learning. IEEE Trans. Emerg. Top. Comput. Intell. 3(5): 357-359 (2019) - [c173]Chang-Shing Lee, Mei-Hui Wang, Li-Chuang Chen, Yusuke Nojima, Tzong-Xiang Huang, Jinseok Woo, Naoyuki Kubota, Eri Sato-Shimokawara, Toru Yamaguchi:
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go. CEC 2019: 793-799 - [c172]Yiping Liu, Hisao Ishibuchi, Yusuke Nojima, Naoki Masuyama, Yuyan Han:
Searching for Local Pareto Optimal Solutions: A Case Study on Polygon-Based Problems. CEC 2019: 896-903 - [c171]Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
A Multiobjective Test Suite with Hexagon Pareto Fronts and Various Feasible Regions. CEC 2019: 2058-2065 - [c170]Hisao Ishibuchi, Ryo Imada, Naoki Masuyama, Yusuke Nojima:
Two-Layered Weight Vector Specification in Decomposition-Based Multi-Objective Algorithms for Many-Objective Optimization Problems. CEC 2019: 2434-2441 - [c169]Hisao Ishibuchi, Ryo Imada, Naoki Masuyama, Yusuke Nojima:
Comparison of Hypervolume, IGD and IGD+ from the Viewpoint of Optimal Distributions of Solutions. EMO 2019: 332-345 - [c168]Yusuke Nojima, Takafumi Fukase, Yiping Liu, Naoki Masuyama, Hisao Ishibuchi:
Constrained multiobjective distance minimization problems. GECCO 2019: 586-594 - [c167]Hisao Ishibuchi, Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima:
Optimal Distributions of Solutions for Hypervolume Maximization on Triangular and Inverted Triangular Pareto Fronts of Four-Objective Problems. SSCI 2019: 1857-1864 - [c166]Naoki Masuyama, Narito Amako, Yusuke Nojima, Yiping Liu, Chu Kiong Loo, Hisao Ishibuchi:
Fast Topological Adaptive Resonance Theory Based on Correntropy Induced Metric. SSCI 2019: 2215-2221 - [c165]Ryuichi Hashimoto, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
Effect of Solution Information Sharing between Tasks on the Search Ability of Evolutionary Multiobjective Multitasking Algorithms. SSCI 2019: 2671-2678 - [i2]Chang-Shing Lee, Mei-Hui Wang, Li-Chuang Chen, Yusuke Nojima, Tzong-Xiang Huang, Jinseok Woo, Naoyuki Kubota, Eri Sato-Shimokawara, Toru Yamaguchi:
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go. CoRR abs/1901.07191 (2019) - 2018
- [j37]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) - [j36]Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, Yusuke Nojima:
A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation. IEEE Trans. Evol. Comput. 22(2): 260-275 (2018) - [j35]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) - [c164]Hisao Ishibuchi, Ryo Imada, Naoki Masuyama, Yusuke Nojima:
Dynamic Specification of a Reference Point for Hypervolume Calculation in SMS-EMOA. CEC 2018: 1-8 - [c163]Hisao Ishibuchi, Takefumi Fukase, Naoki Masuyama, Yusuke Nojima:
Dual-grid model of MOEA/D for evolutionary constrained multiobjective optimization. GECCO 2018: 665-672 - [c162]Ryuichi Hashimoto, Hisao Ishibuchi, Naoki Masuyama, Yusuke Nojima:
Analysis of evolutionary multi-tasking as an island model. GECCO (Companion) 2018: 1894-1897 - [c161]Yiping Liu, Hisao Ishibuchi, Yusuke Nojima, Naoki Masuyama, Ke Shang:
A Double-Niched Evolutionary Algorithm and Its Behavior on Polygon-Based Problems. PPSN (1) 2018: 262-273 - [c160]Yiping Liu, Hisao Ishibuchi, Yusuke Nojima, Naoki Masuyama, Ke Shang:
Improving 1by1EA to Handle Various Shapes of Pareto Fronts. PPSN (1) 2018: 311-322 - [c159]Hisao Ishibuchi, Ryo Imada, Naoki Masuyama, Yusuke Nojima:
Use of Two Reference Points in Hypervolume-Based Evolutionary Multiobjective Optimization Algorithms. PPSN (1) 2018: 384-396 - [c158]Naoki Masuyama, Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization. SMC 2018: 745-750 - [c157]Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
Performance Comparison of Multiobjective Evolutionary Algorithms on Problems with Partially Different Properties from Popular Test Suites. SMC 2018: 769-774 - [c156]Naoki Masuyama, Chu Kiong Loo, Hisao Ishibuchi, Yusuke Nojima, Yiping Lin:
Topological Kernel Bayesian ARTMAP. WAC 2018: 1-5 - 2017
- [j34]Chun-Hao Chen, Chao-Chun Chen, Yusuke Nojima:
An efficient and effective approach for mining a group stock portfolio using mapreduce. Intell. Data Anal. 21(S1): S217-S232 (2017) - [j33]Chun-Hao Chen, Chuan-Kang Ting, Yusuke Nojima:
Special issue on soft computing for big data and social informatics. Soft Comput. 21(11): 2799-2800 (2017) - [j32]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) - [j31]Jesús Alcalá-Fdez, Rafael Alcalá, Sergio González, Yusuke Nojima, Salvador García:
Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification. IEEE Trans. Fuzzy Syst. 25(6): 1376-1390 (2017) - [c155]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 - [c154]Yusuke Nojima, Koki Arahari, Shuji Takemura, Hisao Ishibuchi:
Multiobjective fuzzy genetics-based machine learning based on MOEA/D with its modifications. FUZZ-IEEE 2017: 1-6 - [c153]Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, Yusuke Nojima:
Reference point specification in hypervolume calculation for fair comparison and efficient search. GECCO 2017: 585-592 - [c152]Yusuke Nojima, Yuki Tanigaki, Hisao Ishibuchi:
Multiobjective data mining from solutions by evolutionary multiobjective optimization. GECCO 2017: 617-624 - [c151]Chang-Shing Lee, Chia-Hsiu Kao, Mei-Hui Wang, Sheng-Chi Yang, Yusuke Nojima, Ryosuke Saga, Nan Shuo, Naoyuki Kubota:
FML-based prediction agent and its application to game of Go. IFSA-SCIS 2017: 1-6 - [c150]Yusuke Nojima, Shuji Takemura, Kazuhiro Watanabe, Hisao Ishibuchi:
Michigan-style fuzzy GBML with (1+1)-ES generation update and multi-pattern rule generation. IFSA-SCIS 2017: 1-6 - [c149]Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Performance comparison of EMO algorithms on test problems with different search space shape. IFSA-SCIS 2017: 1-6 - [c148]Ken Doi, Ryo Imada, Yusuke Nojima, Hisao Ishibuchi:
Use of Inverted Triangular Weight Vectors in Decomposition-Based Many-Objective Algorithms. SEAL 2017: 321-333 - [c147]Hisao Ishibuchi, Ryo Imada, Ken Doi, Yusuke Nojima:
Use of inverted triangular weight vectors in decomposition-based multiobjective algorithms. SMC 2017: 373-378 - [i1]Chang-Shing Lee, Mei-Hui Wang, Chia-Hsiu Kao, Sheng-Chi Yang, Yusuke Nojima, Ryosuke Saga, Nan Shuo, Naoyuki Kubota:
FML-based Prediction Agent and Its Application to Game of Go. CoRR abs/1704.04719 (2017) - 2016
- [j30]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
Pareto Fronts of Many-Objective Degenerate Test Problems. IEEE Trans. Evol. Comput. 20(5): 807-813 (2016) - [c146]Takahiko Sudo, Kazushi Goto, Yusuke Nojima, Hisao Ishibuchi:
Further analysis on strange evolution behavior of 7-bit binary string strategies in iterated prisoner's dilemma game. CEC 2016: 335-342 - [c145]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
Sensitivity of performance evaluation results by inverted generational distance to reference points. CEC 2016: 1107-1114 - [c144]Hisao Ishibuchi, Ken Doi, Yusuke Nojima:
Characteristics of many-objective test problems and penalty parameter specification in MOEA/D. CEC 2016: 1115-1122 - [c143]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 - [c142]Yusuke Nojima, Hisao Ishibuchi:
Effects of parallel distributed implementation on the search performance of Pittsburgh-style genetics-based machine learning algorithms. CEC 2016: 2193-2200 - [c141]Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Meta-optimization based multi-objective test problem generation using WFG toolkit. CEC 2016: 2768-2775 - [c140]Hiroyuki Masuda, Yusuke Nojima, Hisao Ishibuchi:
Common properties of scalable multiobjective problems and a new framework of test problems. CEC 2016: 3011-3018 - [c139]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 - [c138]Yusuke Nojima, Hisao Ishibuchi:
Multiobjective fuzzy genetics-based machine learning with a reject option. FUZZ-IEEE 2016: 1405-1412 - [c137]Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, Yusuke Nojima:
Weighted Optimization Framework for Large-scale Multi-objective Optimization. GECCO (Companion) 2016: 83-84 - [c136]Hisao Ishibuchi, Ken Doi, Yusuke Nojima:
Use of Piecewise Linear and Nonlinear Scalarizing Functions in MOEA/D. PPSN 2016: 503-513 - [c135]Takahiro Funakoshi, Yusuke Nojima, Hisao Ishibuchi:
Effects of Different Implementations of a Real Random Number Generator on the Search Behavior of Multiobjective Evolutionary Algorithms. SCIS&ISIS 2016: 172-177 - [c134]Hisao Ishibuchi, Ken Doi, Yusuke Nojima:
Reference point specification in MOEA/D for multi-objective and many-objective problems. SMC 2016: 4015-4020 - [c133]Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, Yusuke Nojima:
Mutation operators based on variable grouping for multi-objective large-scale optimization. SSCI 2016: 1-8 - 2015
- [j29]Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima:
Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems. IEEE Trans. Evol. Comput. 19(2): 264-283 (2015) - [c132]Yusuke Nojima, Yuji Takahashi, Hisao Ishibuchi:
Application of Parallel Distributed Implementation to Multiobjective Fuzzy Genetics-Based Machine Learning. ACIIDS (1) 2015: 462-471 - [c131]Yuji Takahashi, Yusuke Nojima, Hisao Ishibuchi:
Rotation effects of objective functions in parallel distributed multiobjective fuzzy genetics-based machine learning. ASCC 2015: 1-6 - [c130]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 - [c129]Takahiko Sudo, Kazushi Goto, Yusuke Nojima, Hisao Ishibuchi:
Effects of ensemble action selection with different usage of player's memory resource on the evolution of cooperative strategies for iterated prisoner's dilemma game. CEC 2015: 1505-1512 - [c128]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
Comparing solution sets of different size in evolutionary many-objective optimization. CEC 2015: 2859-2866 - [c127]Yusuke Nojima, Kazuhiro Watanabe, Hisao Ishibuchi:
Effects of heuristic rule generation from multiple patterns in multiobjective fuzzy genetics-Based machine learning. CEC 2015: 2996-3003 - [c126]Takahiko Sudo, Kazushi Goto, Yusuke Nojima, Hisao Ishibuchi:
Strange evolution behavior of 7-bit binary string strategies in iterated prisoner's dilemma game. CEC 2015: 3346-3353 - [c125]Hisao Ishibuchi, Hiroyuki Masuda, Yuki Tanigaki, Yusuke Nojima:
Modified Distance Calculation in Generational Distance and Inverted Generational Distance. EMO (2) 2015: 110-125 - [c124]Hisao Ishibuchi, Yusuke Nojima:
Handling a training dataset as a black-box model for privacy preserving in fuzzy GBML algorithms. FUZZ-IEEE 2015: 1-8 - [c123]Yusuke Nojima, Kazuhiro Watanabe, Hisao Ishibuchi:
Simple modifications on heuristic rule generation and rule evaluation in Michigan-style fuzzy genetics-based machine learning. FUZZ-IEEE 2015: 1-8 - [c122]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
A Study on Performance Evaluation Ability of a Modified Inverted Generational Distance Indicator. GECCO 2015: 695-702 - [c121]Hisao Ishibuchi, Ken Doi, Hiroyuki Masuda, Yusuke Nojima:
Relation Between Weight Vectors and Solutions in MOEA/D. SSCI 2015: 861-868 - [c120]Yusuke Nojima, Chun-Hao Chen:
Genetic fuzzy systems and its application to data mining. TAAI 2015: 33 - [c119]Yusuke Nojima, Kazuhiro Watanabe, Hisao Ishibuchi:
Variants of heuristic rule generation from multiple patterns in Michigan-style fuzzy genetics-based machine learning. TAAI 2015: 427-432 - [p7]Hisao Ishibuchi, Yusuke Nojima:
Multiobjective Genetic Fuzzy Systems. Handbook of Computational Intelligence 2015: 1479-1498 - 2014
- [j28]Chin Hooi Tan, Keem Siah Yap, Hisao Ishibuchi, Yusuke Nojima, Hwa Jen Yap:
Application of Fuzzy Inference Rules to Early Semi-automatic Estimation of Activity Duration in Software Project Management. IEEE Trans. Hum. Mach. Syst. 44(5): 678-688 (2014) - [c118]Takahiko Sudo, Yusuke Nojima, Hisao Ishibuchi:
Effects of ensemble action selection on the evolution of iterated prisoner's dilemma game strategies. IEEE Congress on Evolutionary Computation 2014: 1195-1201 - [c117]Hiroyuki Masuda, Yusuke Nojima, Hisao Ishibuchi:
Visual examination of the behavior of EMO algorithms for many-objective optimization with many decision variables. IEEE Congress on Evolutionary Computation 2014: 2633-2640 - [c116]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 - [c115]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 - [c114]Yuji Takahashi, Yusuke Nojima, Hisao Ishibuchi:
Hybrid fuzzy genetics-based machine learning with entropy-based inhomogeneous interval discretization. FUZZ-IEEE 2014: 1512-1517 - [c113]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
Meta-level multi-objective formulations of set optimization for multi-objective optimization problems: multi-reference point approach to hypervolume maximization. GECCO (Companion) 2014: 89-90 - [c112]Hisao Ishibuchi, Takahiko Sudo, Yusuke Nojima:
Archive Management in Interactive Evolutionary Computation with Minimum Requirement for Human User's Fitness Evaluation Ability. ICAISC (1) 2014: 360-371 - [c111]Hisao Ishibuchi, Yuki Tanigaki, Hiroyuki Masuda, Yusuke Nojima:
Distance-Based Analysis of Crossover Operators for Many-Objective Knapsack Problems. PPSN 2014: 600-610 - [c110]Yuki Tanigaki, Kaname Narukawa, Yusuke Nojima, Hisao Ishibuchi:
Preference-based NSGA-II for many-objective knapsack problems. SCIS&ISIS 2014: 637-642 - [c109]Yusuke Nojima, Yuji Takahashi, Hisao Ishibuchi:
Genetic lateral tuning of membership functions as post-processing for hybrid fuzzy genetics-based machine learning. SCIS&ISIS 2014: 667-672 - [c108]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
Selecting a small number of non-dominated solutions to be presented to the decision maker. SMC 2014: 3816-3821 - 2013
- [j27]Rafael Alcalá, Yusuke Nojima, Hisao Ishibuchi, Francisco Herrera:
Special Issue on "Evolutionary Fuzzy Systems" EFSs. Knowl. Based Syst. 54: 1-2 (2013) - [j26]Hisao Ishibuchi, Yusuke Nojima:
Repeated double cross-validation for choosing a single solution in evolutionary multi-objective fuzzy classifier design. Knowl. Based Syst. 54: 22-31 (2013) - [j25]Michela Fazzolari, Rafael Alcalá, Yusuke Nojima, Hisao Ishibuchi, Francisco Herrera:
A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions. IEEE Trans. Fuzzy Syst. 21(1): 45-65 (2013) - [j24]Hisao Ishibuchi, Shingo Mihara, Yusuke Nojima:
Parallel Distributed Hybrid Fuzzy GBML Models With Rule Set Migration and Training Data Rotation. IEEE Trans. Fuzzy Syst. 21(2): 355-368 (2013) - [c107]Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima:
Learning from multiple data sets with different missing attributes and privacy policies: Parallel distributed fuzzy genetics-based machine learning approach. IEEE BigData 2013: 63-70 - [c106]Yusuke Nojima, Xian-Hua Han, Kazuki Taniguchi, Yen-Wei Chen:
High frequency compensated face hallucination with total variation constraint. BMEI 2013: 831-835 - [c105]Hisao Ishibuchi, Takahiko Sudo, Koichiro Hoshino, Yusuke Nojima:
Evolution of cooperative strategies for iterated prisoner's dilemma on networks. CASoN 2013: 32-37 - [c104]Hisao Ishibuchi, Masakazu Yamane, Naoya Akedo, Yusuke Nojima:
Many-objective and many-variable test problems for visual examination of multiobjective search. IEEE Congress on Evolutionary Computation 2013: 1491-1498 - [c103]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 - [c102]Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima:
Effects of duplicated objectives in many-objective optimization problems on the search behavior of hypervolume-based evolutionary algorithms. MCDM 2013: 25-32 - [c101]Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima:
Difficulty in Evolutionary Multiobjective Optimization of Discrete Objective Functions with Different Granularities. EMO 2013: 230-245 - [c100]Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima:
Relation between Neighborhood Size and MOEA/D Performance on Many-Objective Problems. EMO 2013: 459-474 - [c99]Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima:
Rule weight update in parallel distributed fuzzy genetics-based machine learning with data rotation. FUZZ-IEEE 2013: 1-8 - [c98]Michela Fazzolari, Rafael Alcalá, Yusuke Nojima, Hisao Ishibuchi, Francisco Herrera:
Improving a fuzzy association rule-based classification model by granularity learning based on heuristic measures over multiple granularities. GEFS 2013: 44-51 - [c97]Yusuke Nojima, Hisao Ishibuchi:
Multiobjective genetic fuzzy rule selection with fuzzy relational rules. GEFS 2013: 60-67 - [c96]Hisao Ishibuchi, Yusuke Nojima:
Difficulties in choosing a single final classifier from non-dominated solutions in multiobjective fuzzy genetics-based machine learning. IFSA/NAFIPS 2013: 1203-1208 - [c95]Hisao Ishibuchi, Koichiro Hoshino, Yusuke Nojima:
Neighborhood Specification for Game Strategy Evolution in a Spatial Iterated Prisoner's Dilemma Game. LION 2013: 215-230 - [c94]Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima:
A Study on the Specification of a Scalarizing Function in MOEA/D for Many-Objective Knapsack Problems. LION 2013: 231-246 - [c93]Hisao Ishibuchi, Takahiko Sudo, Koichiro Hoshino, Yusuke Nojima:
Effects of the Number of Opponents on the Evolution of Cooperation in the Iterated Prisoner's Dilemma. SMC 2013: 2001-2006 - 2012
- [j23]Rafael Alcalá, Yusuke Nojima, Hisao Ishibuchi, Francisco Herrera:
Special Issue on Evolutionary Fuzzy Systems. Int. J. Comput. Intell. Syst. 5(2): 209-211 (2012) - [c92]Hisao Ishibuchi, Koichiro Hoshino, Yusuke Nojima:
Strategy evolution in a spatial IPD game where each agent is not allowed to play against itself. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c91]Hisao Ishibuchi, Koichiro Hoshino, Yusuke Nojima:
Evolution of strategies in a spatial IPD game with a number of different representation schemes. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c90]Yusuke Nojima, Shingo Mihara, Hisao Ishibuchi:
Application of parallel distributed genetics-based machine learning to imbalanced data sets. FUZZ-IEEE 2012: 1-6 - [c89]Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima:
Effects of discrete objective functions with different granularities on the search behavior of EMO algorithms. GECCO 2012: 481-488 - [c88]Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima:
Recombination of Similar Parents in SMS-EMOA on Many-Objective 0/1 Knapsack Problems. PPSN (2) 2012: 132-142 - [c87]Masakazu Yamane, Akihito Ueda, Naoshi Tadokoro, Yusuke Nojima, Hisao Ishibuchi:
Comparison of different fitness functions in genetic fuzzy rule selection. SCIS&ISIS 2012: 1046-1051 - [c86]Hisao Ishibuchi, Masakazu Yamane, Naoya Akedo, Yusuke Nojima:
Two-objective solution set optimization to maximize hypervolume and decision space diversity in multiobjective optimization. SCIS&ISIS 2012: 1871-1876 - [c85]Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima:
Ensemble Fuzzy Rule-Based Classifier Design by Parallel Distributed Fuzzy GBML Algorithms. SEAL 2012: 93-103 - [c84]Yusuke Nojima, Yasuhito Asano, Masatoshi Yoshikawa:
Greedy Approximation Algorithms for Generalized Maximum Flow Problem towards Relation Extraction in Information Networks. TJJCCGG 2012: 132-142 - 2011
- [j22]Hisao Ishibuchi, Yutaka Kaisho, Yusuke Nojima:
Design of Linguistically Interpretable Fuzzy Rule-Based Classifiers: A Short Review and Open Questions. J. Multiple Valued Log. Soft Comput. 17(2-3): 101-134 (2011) - [j21]Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima:
Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization. Soft Comput. 15(9): 1749-1767 (2011) - [j20]Yusuke Nojima, Rafael Alcalá, Hisao Ishibuchi, Francisco Herrera:
Special issue on evolutionary fuzzy systems. Soft Comput. 15(12): 2299-2301 (2011) - [j19]Rafael Alcalá, Yusuke Nojima, Francisco Herrera, Hisao Ishibuchi:
Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions. Soft Comput. 15(12): 2303-2318 (2011) - [j18]Hisao Ishibuchi, Yusuke Nakashima, Yusuke Nojima:
Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning. Soft Comput. 15(12): 2415-2434 (2011) - [j17]Hisao Ishibuchi, Hiroyuki Ohyanagi, Yusuke Nojima:
Evolution of Strategies With Different Representation Schemes in a Spatial Iterated Prisoner's Dilemma Game. IEEE Trans. Comput. Intell. AI Games 3(1): 67-82 (2011) - [c83]Hisao Ishibuchi, Naoya Akedo, Hiroyuki Ohyanagi, Yusuke Nojima:
Behavior of EMO algorithms on many-objective optimization problems with correlated objectives. IEEE Congress on Evolutionary Computation 2011: 1465-1472 - [c82]Hisao Ishibuchi, Keisuke Takahashi, Kouichirou Hoshino, Junpei Maeda, Yusuke Nojima:
Effects of configuration of agents with different strategy representations on the evolution of cooperative behavior in a spatial IPD game. CIG 2011: 313-320 - [c81]Hisao Ishibuchi, Naoya Akedo, Hiroyuki Ohyanagi, Yasuhiro Hitotsuyanagi, Yusuke Nojima:
Many-objective test problems with multiple Pareto optimal regions in a decision space. MCDM 2011: 113-120 - [c80]Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Hiroyuki Ohyanagi, Yusuke Nojima:
Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D. EMO 2011: 166-181 - [c79]Yusuke Nojima, Shinya Nishikawa, Hisao Ishibuchi:
A meta-fuzzy classifier for specifying appropriate fuzzy partitions by genetic fuzzy rule selection with data complexity measures. FUZZ-IEEE 2011: 264-271 - [c78]Hisao Ishibuchi, Yusuke Nojima:
Toward quantitative definition of explanation ability of fuzzy rule-based classifiers. FUZZ-IEEE 2011: 549-556 - [c77]Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima:
A many-objective test problem for visually examining diversity maintenance behavior in a decision space. GECCO 2011: 649-656 - [c76]Hisao Ishibuchi, Yusuke Nakashima, Yusuke Nojima:
Double cross-validation for performance evaluation of multi-objective genetic fuzzy systems. GEFS 2011: 31-38 - [c75]Yusuke Nojima, Hisao Ishibuchi:
Mobile Robot Controller Design by Evolutionary Multiobjective Optimization in Multiagent Environments. ICIRA (2) 2011: 515-524 - [c74]Hisao Ishibuchi, Shingo Mihara, Yusuke Nojima:
Training Data Subdivision and Periodical Rotation in Hybrid Fuzzy Genetics-Based Machine Learning. ICMLA (1) 2011: 229-234 - 2010
- [j16]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Diversity Improvement by Non-Geometric Binary Crossover in Evolutionary Multiobjective Optimization. IEEE Trans. Evol. Comput. 14(6): 985-998 (2010) - [c73]Yusuke Nojima, Shingo Mihara, Hisao Ishibuchi:
Ensemble classifier design by parallel distributed implementation of genetic fuzzy rule selection for large data sets. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c72]Hisao Ishibuchi, Yusuke Nakashima, Yusuke Nojima:
Effects of fine fuzzy partitions on the generalization ability of evolutionary multi-objective fuzzy rule-based classifiers. FUZZ-IEEE 2010: 1-8 - [c71]Yusuke Nojima, Yutaka Kaisho, Hisao Ishibuchi:
Accuracy improvement of genetic fuzzy rule selection with candidate rule addition and membership tuning. FUZZ-IEEE 2010: 1-8 - [c70]Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima:
Simultaneous use of different scalarizing functions in MOEA/D. GECCO 2010: 519-526 - [c69]Hisao Ishibuchi, Noritaka Tsukamoto, Yuji Sakane, Yusuke Nojima:
Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions. GECCO 2010: 527-534 - [c68]Hisao Ishibuchi, Yusuke Nakashima, Yusuke Nojima:
Simple changes in problem formulations make a difference in multiobjective genetic fuzzy systems. GEFS 2010: 3-8 - [c67]Yusuke Nojima, Hisao Ishibuchi, Shingo Mihara:
Use of very small training data subsets in parallel distributed genetic fuzzy rule selection. GEFS 2010: 27-32 - [c66]Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yusuke Nakashima, Yusuke Nojima:
Multiobjectivization from two objectives to four objectives in evolutionary multi-objective optimization algorithms. NaBIC 2010: 502-507 - [c65]Shinya Nishikawa, Yusuke Nojima, Hisao Ishibuchi:
Appropriate granularity specification for fuzzy classifier design by data complexity measures. NaBIC 2010: 691-696 - [c64]Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto, Yusuke Nojima:
Many-Objective Test Problems to Visually Examine the Behavior of Multiobjective Evolution in a Decision Space. PPSN (2) 2010: 91-100 - [c63]Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yoshihiko Wakamatsu, Yusuke Nojima:
How to Choose Solutions for Local Search in Multiobjective Combinatorial Memetic Algorithms. PPSN (1) 2010: 516-525 - [c62]Yusuke Nojima, Shingo Mihara, Hisao Ishibuchi:
Parallel Distributed Implementation of Genetics-Based Machine Learning for Fuzzy Classifier Design. SEAL 2010: 309-318
2000 – 2009
- 2009
- [j15]Noritaka Tsukamoto, Yusuke Nojima, Hisao Ishibuchi:
Effects of nongeometric binary crossover on multiobjective 0/1 knapsack problems. Artif. Life Robotics 13(2): 434-437 (2009) - [j14]Yoshihiro Hamada, Yusuke Nojima, Hisao Ishibuchi:
Use of multi-objective genetic rule selection for examining the effectiveness of inter-vehicle communication in traffic simulations. Artif. Life Robotics 14(3): 410-413 (2009) - [j13]Hiroyuki Ohyanagi, Yoshihiko Wakamatsu, Yusuke Nakashima, Yusuke Nojima, Hisao Ishibuchi:
Evolution of cooperative behavior among heterogeneous agents with different strategy representations in an iterated prisoner's dilemma game. Artif. Life Robotics 14(3): 414-417 (2009) - [j12]Yusuke Nojima, Hisao Ishibuchi:
Incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems. Artif. Life Robotics 14(3): 418-421 (2009) - [j11]Rafael Alcalá, Yusuke Nojima:
Special issue on genetic fuzzy systems: new advances. Evol. Intell. 2(1-2): 1-3 (2009) - [j10]Yusuke Nojima, Hisao Ishibuchi, Isao Kuwajima:
Parallel distributed genetic fuzzy rule selection. Soft Comput. 13(5): 511-519 (2009) - [j9]Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto, Yusuke Nojima:
Use of biased neighborhood structures in multiobjective memetic algorithms. Soft Comput. 13(8-9): 795-810 (2009) - [c61]Hisao Ishibuchi, Noritaka Tsukamoto, Yuji Sakane, Yusuke Nojima:
Hypervolume approximation using achievement scalarizing functions for evolutionary many-objective optimization. IEEE Congress on Evolutionary Computation 2009: 530-537 - [c60]Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima:
Effects of using two neighborhood structures on the performance of cellular evolutionary algorithms for many-objective optimization. IEEE Congress on Evolutionary Computation 2009: 2508-2515 - [c59]Yusuke Nojima, Hisao Ishibuchi:
Interactive genetic fuzzy rule selection through evolutionary multiobjective optimization with user preference. MCDM 2009: 141-148 - [c58]Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima:
Adaptation of Scalarizing Functions in MOEA/D: An Adaptive Scalarizing Function-Based Multiobjective Evolutionary Algorithm. EMO 2009: 438-452 - [c57]Hisao Ishibuchi, Yusuke Nojima:
Discussions on Interpretability of Fuzzy Systems using Simple Examples. IFSA/EUSFLAT Conf. 2009: 1649-1654 - [c56]Yusuke Nojima, Hisao Ishibuchi:
Interactive Fuzzy Modeling by Evolutionary Multiobjective Optimization with User Preference. IFSA/EUSFLAT Conf. 2009: 1839-1844 - [c55]Hisao Ishibuchi, Hiroyuki Ohyanagi, Yusuke Nojima:
Evolution of cooperative behavior in a spatial iterated prisoner's dilemma game with different representation schemes of game strategies. FUZZ-IEEE 2009: 1568-1573 - [c54]Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima:
Selecting a small number of representative non-dominated solutions by a hypervolume-based solution selection approach. FUZZ-IEEE 2009: 1609-1614 - [c53]Rafael Alcalá, Yusuke Nojima, Francisco Herrera, Hisao Ishibuchi:
Generating single granularity-based fuzzy classification rules for multiobjective genetic fuzzy rule selection. FUZZ-IEEE 2009: 1718-1723 - [c52]Hisao Ishibuchi, Yusuke Nakashima, Yusuke Nojima:
Search ability of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning. FUZZ-IEEE 2009: 1724-1729 - [c51]Hisao Ishibuchi, Yutaka Kaisho, Yusuke Nojima:
Complexity, interpretability and explanation capability of fuzzy rule-based classifiers. FUZZ-IEEE 2009: 1730-1735 - [c50]Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima:
Single-objective and multi-objective formulations of solution selection for hypervolume maximization. GECCO 2009: 1831-1832 - [c49]Yusuke Nojima, Hisao Ishibuchi:
Effects of Data Reduction on the Generalization Ability of Parallel Distributed Genetic Fuzzy Rule Selection. ISDA 2009: 96-101 - [c48]Yusuke Nojima:
Multi-objective behavior coordination based on sensory network for multiple mobile robots. RiiSS 2009: 66-72 - [c47]Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima:
Evolutionary Many-Objective Optimization by NSGA-II and MOEA/D with Large Populations. SMC 2009: 1758-1763 - [c46]Yusuke Nojima, Yusuke Nakashima, Hisao Ishibuchi:
Effects of the Use of Multiple Fuzzy Partitions on the Search Ability of Multiobjective Fuzzy Genetics-Based Machine Learning. SoCPaR 2009: 341-346 - [c45]Yuki Tsujimoto, Yasuhiro Hitotsuyanagi, Yusuke Nojima, Hisao Ishibuchi:
Effects of Including Single-Objective Optimal Solutions in an Initial Population on Evolutionary Multiobjective Optimization. SoCPaR 2009: 352-357 - [c44]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Empirical Analysis of Using Weighted Sum Fitness Functions in NSGA-II for Many-Objective 0/1 Knapsack Problems. UKSim 2009: 71-76 - 2008
- [j8]Isao Kuwajima, Yusuke Nojima, Hisao Ishibuchi:
Effects of constructing fuzzy discretization from crisp discretization for rule-based classifiers. Artif. Life Robotics 13(1): 294-297 (2008) - [j7]Isao Kuwajima, Yusuke Nojima, Hisao Ishibuchi:
Obtaining accurate classifiers with Pareto-optimal and near Pareto-optimal rules. Artif. Life Robotics 13(1): 315-319 (2008) - [j6]Hisao Ishibuchi, Kaname Narukawa, Noritaka Tsukamoto, Yusuke Nojima:
An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization. Eur. J. Oper. Res. 188(1): 57-75 (2008) - [c43]Hisao Ishibuchi, Yusuke Nojima:
Evolutionary Multiobjective Fuzzy System Design. BIONETICS 2008: 30 - [c42]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Evolutionary many-objective optimization: A short review. IEEE Congress on Evolutionary Computation 2008: 2419-2426 - [c41]Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yusuke Nojima:
Scalability of multiobjective genetic local search to many-objective problems: Knapsack problem case studies. IEEE Congress on Evolutionary Computation 2008: 3586-3593 - [c40]Isao Kuwajima, Hisao Ishibuchi, Yusuke Nojima:
Effectiveness of designing fuzzy rule-based classifiers from Pareto-optimal rules. FUZZ-IEEE 2008: 1185-1192 - [c39]Hisao Ishibuchi, Noritaka Tsukamoto, Yasuhiro Hitotsuyanagi, Yusuke Nojima:
Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. GECCO 2008: 649-656 - [c38]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Maintaining the diversity of solutions by non-geometric binary crossover: a worst one-max solver competition case study. GECCO 2008: 1111-1112 - [c37]Hisao Ishibuchi, Yutaka Kaisho, Yusuke Nojima:
Designing fuzzy rule-based classifiers that can visually explain their classification results to human users. GEFS 2008: 5-10 - [c36]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Evolutionary many-objective optimization. GEFS 2008: 47-52 - [c35]Yusuke Nojima, Hisao Ishibuchi:
Effects of Diversity Measures on the Design of Ensemble Classifiers by Multiobjective Genetic Fuzzy Rule Selection with a Multi-classifier Coding Scheme. HAIS 2008: 755-763 - [c34]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Examining the Effect of Elitism in Cellular Genetic Algorithms Using Two Neighborhood Structures. PPSN 2008: 458-467 - [c33]Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto, Yusuke Nojima:
Use of Heuristic Local Search for Single-Objective Optimization in Multiobjective Memetic Algorithms. PPSN 2008: 743-752 - [c32]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Use of Local Ranking in Cellular Genetic Algorithms with Two Neighborhood Structures. SEAL 2008: 309-318 - [c31]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Behavior of Evolutionary Many-Objective Optimization. UKSim 2008: 266-271 - [p6]Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima:
Multiobjective Classification Rule Mining. Multiobjective Problem Solving from Nature 2008: 219-240 - [p5]Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima:
Evolutionary Multi-objective Rule Selection for Classification Rule Mining. Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases 2008: 47-70 - [p4]Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima:
Evolutionary Multiobjective Design of Fuzzy Rule-Based Classifiers. Computational Intelligence: A Compendium 2008: 641-685 - [p3]Hisao Ishibuchi, Yusuke Nojima:
Pattern Classification with Linguistic Rules. Fuzzy Sets and Their Extensions: Representation, Aggregation and Models 2008: 377-395 - 2007
- [j5]Hisao Ishibuchi, Yusuke Nojima:
Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int. J. Approx. Reason. 44(1): 4-31 (2007) - [j4]Yusuke Nojima, Hisao Ishibuchi:
Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design. Int. J. Hybrid Intell. Syst. 4(3): 157-169 (2007) - [c30]Ken Ohara, Yusuke Nojima, Hisao Ishibuchi:
A Study on Traffic Information Sharing Through Inter-Vehicle Communication. ISIC 2007: 670-675 - [c29]Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yusuke Nojima:
An empirical study on the specification of the local search application probability in multiobjective memetic algorithms. IEEE Congress on Evolutionary Computation 2007: 2788-2795 - [c28]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Iterative approach to indicator-based multiobjective optimization. IEEE Congress on Evolutionary Computation 2007: 3967-3974 - [c27]Ken Ohara, Yusuke Nojima, Yumeka Kitano, Hisao Ishibuchi:
Effects of spatial structures on evolution of iterated prisoner's dilemma game strategies with probabilistic decision making. IEEE Congress on Evolutionary Computation 2007: 4051-4058 - [c26]Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima:
Relation between Pareto-Optimal Fuzzy Rules and Pareto-Optimal Fuzzy Rule Sets. MCDM 2007: 42-49 - [c25]Hisao Ishibuchi, Yusuke Nojima:
Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization. EMO 2007: 51-65 - [c24]Yusuke Nojima, Isao Kuwajima, Hisao Ishibuchi:
Data Set Subdivision for Parallel Distributed Implementation of Genetic Fuzzy Rule Selection. FUZZ-IEEE 2007: 1-6 - [c23]Hisao Ishibuchi, Yusuke Nojima, Noritaka Tsukamoto, Ken Ohara:
Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization. GECCO 2007: 829-836 - [c22]Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima:
Prescreening of Candidate Rules Using Association Rule Mining and Pareto-optimality in Genetic Rule Selection. KES (2) 2007: 509-516 - [c21]Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima:
Choosing extreme parents for diversity improvement in evolutionary multiobjective optimization algorithms. SMC 2007: 1946-1951 - [p2]Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima:
Use of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection. Analysis and Design of Intelligent Systems using Soft Computing Techniques 2007: 387-396 - 2006
- [j3]Hisao Ishibuchi, Yusuke Nojima:
Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers. Int. J. Hybrid Intell. Syst. 3(3): 129-145 (2006) - [j2]Naoyuki Kubota, Yusuke Nojima, Fumio Kojima, Toshio Fukuda:
Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot. Soft Comput. 10(10): 891-901 (2006) - [c20]Hisao Ishibuchi, Yusuke Nojima, Tsutomu Doi:
Comparison between Single-Objective and Multi-Objective Genetic Algorithms: Performance Comparison and Performance Measures. IEEE Congress on Evolutionary Computation 2006: 1143-1150 - [c19]Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima:
Fuzzy Data Mining by Heuristic Rule Extraction and Multiobjective Genetic Rule Selection. FUZZ-IEEE 2006: 1633-1640 - [c18]Hisao Ishibuchi, Yusuke Nojima, Kaname Narukawa, Tsutomu Doi:
Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms. GECCO 2006: 741-742 - [c17]Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima:
Multiobjective genetic rule selection as a data mining postprocessing procedure. GECCO 2006: 1591-1592 - [c16]Yusuke Nojima, Hisao Ishibuchi:
Designing Fuzzy Ensemble Classifiers by Evolutionary Multiobjective Optimization with an Entropy-Based Diversity Criterion. HIS 2006: 59 - [c15]Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima:
Finding Simple Fuzzy Classification Systems with High Interpretability Through Multiobjective Rule Selection. KES (2) 2006: 86-93 - [c14]Hisao Ishibuchi, Tsutomu Doi, Yusuke Nojima:
Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms. PPSN 2006: 493-502 - [c13]Hisao Ishibuchi, Tsutomu Doi, Yusuke Nojima:
Effects of Using Two Neighborhood Structures in Cellular Genetic Algorithms for Function Optimization. PPSN 2006: 949-958 - [p1]Hisao Ishibuchi, Yusuke Nojima:
Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection. Multi-Objective Machine Learning 2006: 507-530 - 2005
- [c12]Yusuke Nojima, Kaname Narukawa, Shiori Kaige, Hisao Ishibuchi:
Effects of Removing Overlapping Solutions on the Performance of the NSGA-II Algorithm. EMO 2005: 341-354 - [c11]Hisao Ishibuchi, Yusuke Nojima:
Multiobjective Formulations of Fuzzy Rule-Based Classification System Design. EUSFLAT Conf. 2005: 285-290 - [c10]Hisao Ishibuchi, Yusuke Nojima:
Comparison between Fuzzy and Interval Partitions in Evolutionary Multiobjective Design of Rule-Based Classification Systems. FUZZ-IEEE 2005: 430-435 - [c9]Kaname Narukawa, Yusuke Nojima, Hisao Ishibuchi:
Modification of Evolutionary Multiobjective Optimization Algorithms for Multiobjective Design of Fuzzy Rule-Based Classification Systems. FUZZ-IEEE 2005: 809-814 - [c8]Hisao Ishibuchi, Kaname Narukawa, Yusuke Nojima:
An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization. GECCO 2005: 817-824 - [c7]Hisao Ishibuchi, Yusuke Nojima:
Performance Evaluation of Evolutionary Multiobjective Approaches to the Design of Fuzzy Rule-Based Ensemble Classifiers. HIS 2005: 271-276 - 2004
- [c6]Yusuke Nojima, Naoyuki Kubota, Fumio Kojima:
Trajectory generation and accumulation for partner robots based on structured learning. IEEE Congress on Evolutionary Computation 2004: 2224-2229 - [c5]Naoyuki Kubota, Yusuke Nojima, Fumio Kojima:
Imitative behavior generation for a vision-based partner robot. IROS 2004: 3080-3085 - 2003
- [c4]Yusuke Nojima, Fumio Kojima, Naoyuki Kubota:
Trajectory generation for human-friendly behavior of partner robot using fuzzy evaluating interactive genetic algorithm. CIRA 2003: 306-311 - [c3]Yusuke Nojima, Fumio Kojima, Naoyuki Kubota:
Local episode-based learning of multi-objective behavior coordination for a mobile robot in dynamic environments. FUZZ-IEEE 2003: 307-312 - 2000
- [j1]Naoyuki Kubota, Yusuke Nojima, Fumio Kojima, Toshio Fukuda, Susumu Shibata:
Path Planning and Control for a Flexible Transfer System. J. Robotics Mechatronics 12(2): 103-109 (2000) - [c2]Naoyuki Kubota, Yusuke Nojima, Norio Baba, Fumio Kojima, Toshio Fukuda:
Evolving pet robot with emotional model. CEC 2000: 1231-1237 - [c1]Naoyuki Kubota, Yusuke Nojima, Fumio Kojima, Toshio Fukuda:
Multi-Objective Behavior Coordinate for a Mobile Robot with Fuzzy Neural Networks. IJCNN (6) 2000: 311-316
Coauthor Index
[j62] [j60] [j56] [c194] [c193] [i12] [i11] [j52] [j51] [j49] [c192] [c191] [c190] [i10] [i9] [i8] [j47] [j46] [c185] [i7] [j42] [j41] [j40] [c183] [c182] [c181] [c180] [c179] [c177] [c176] [c174] [j39] [c172] [c171] [c170] [c169] [c168] [c167] [c166] [c165] [j37] [j36] [j35] [c164] [c163] [c162] [c161] [c160] [c159] [c158] [c157] [c156] [j32] [c155] [c154] [c153] [c152] [c150] [c149] [c148] [c147] [j30] [c146] [c145] [c144] [c143] [c142] [c141] [c140] [c139] [c138] [c137] [c136] [c135] [c134] [c133] [j29] [c132] [c131] [c130] [c129] [c128] [c127] [c126] [c125] [c124] [c123] [c122] [c121] [c119] [p7] [j28] [c118] [c117] [c116] [c115] [c114] [c113] [c112] [c111] [c110] [c109] [c108] [j27] [j26] [j25] [j24] [c107] [c105] [c104] [c103] [c102] [c101] [c100] [c99] [c98] [c97] [c96] [c95] [c94] [c93] [j23] [c92] [c91] [c90] [c89] [c88] [c87] [c86] [c85] [j22] [j21] [j20] [j19] [j18] [j17] [c83] [c82] [c81] [c80] [c79] [c78] [c77] [c76] [c75] [c74] [j16] [c73] [c72] [c71] [c70] [c69] [c68] [c67] [c66] [c65] [c64] [c63] [c62] [j15] [j14] [j13] [j12] [j10] [j9] [c61] [c60] [c59] [c58] [c57] [c56] [c55] [c54] [c53] [c52] [c51] [c50] [c49] [c47] [c46] [c45] [c44] [j8] [j7] [j6] [c43] [c42] [c41] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [p6] [p5] [p4] [p3] [j5] [j4] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [p2] [j3] [c20] [c19] [c18] [c17] [c16] [c15] [c14] [c13] [p1] [c12] [c11] [c10] [c9] [c8] [c7]
[j62] [c201] [c200] [c199] [c197] [c196] [i13] [j60] [j56] [j55] [c194] [c193] [i12] [i11] [j52] [c192] [c191] [c190] [c189] [c188] [c187] [c186] [i10] [i9] [i8] [c185] [c184] [i7] [i6] [j42] [j41] [c183] [c182] [c181] [c180] [c179] [c177] [c176] [c174] [j39] [c172] [c171] [c170] [c169] [c168] [c167] [c166] [c165] [c164] [c163] [c162] [c161] [c160] [c159] [c158] [c157] [c156]
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