Google
Google Scholar
Semantic Scholar
MS Academic
CiteSeerX
ORCIDYusuke Nojima
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2020
[j44]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)- [j43]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) - [j42]Irene Díaz, Yusuke Nojima:
Fuzzy sets for decision making in emerging domains. Fuzzy Sets Syst. 395: 197-198 (2020) - [j41]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) - [j40]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) - [j39]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) - [c181]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 - [c180]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 - [c179]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 - [c178]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 - [c177]Yuichi Omozaki, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
Multiobjective Fuzzy Genetics-Based Machine Learning for Multi-Label Classification. FUZZ-IEEE 2020: 1-8 - [c176]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 - [c175]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 - [c174]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 - [c173]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
- [j38]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) - [j37]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) - [c172]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 - [c171]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 - [c170]Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi:
A Multiobjective Test Suite with Hexagon Pareto Fronts and Various Feasible Regions. CEC 2019: 2058-2065 - [c169]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 - [c168]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 - [c167]Yusuke Nojima, Takafumi Fukase, Yiping Liu, Naoki Masuyama, Hisao Ishibuchi:
Constrained multiobjective distance minimization problems. GECCO 2019: 586-594 - [c166]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 - [c165]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 - [c164]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
- [j36]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) - [j35]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) - [j34]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) - [c163]Hisao Ishibuchi, Ryo Imada, Naoki Masuyama, Yusuke Nojima:
Dynamic Specification of a Reference Point for Hypervolume Calculation in SMS-EMOA. CEC 2018: 1-8 - [c162]Hisao Ishibuchi, Takefumi Fukase, Naoki Masuyama, Yusuke Nojima:
Dual-grid model of MOEA/D for evolutionary constrained multiobjective optimization. GECCO 2018: 665-672 - [c161]Ryuichi Hashimoto, Hisao Ishibuchi, Naoki Masuyama, Yusuke Nojima:
Analysis of evolutionary multi-tasking as an island model. GECCO (Companion) 2018: 1894-1897 - [c160]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 - [c159]Yiping Liu, Hisao Ishibuchi, Yusuke Nojima, Naoki Masuyama, Ke Shang:
Improving 1by1EA to Handle Various Shapes of Pareto Fronts. PPSN (1) 2018: 311-322 - [c158]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 - [c157]Naoki Masuyama, Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization. SMC 2018: 745-750 - [c156]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 - [c155]Naoki Masuyama, Chu Kiong Loo, Hisao Ishibuchi, Yusuke Nojima, Yiping Lin:
Topological Kernel Bayesian ARTMAP. WAC 2018: 1-5 - 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) - [c154]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 - [c153]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 - [c152]Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, Yusuke Nojima:
Reference point specification in hypervolume calculation for fair comparison and efficient search. GECCO 2017: 585-592 - [c151]Yusuke Nojima, Yuki Tanigaki, Hisao Ishibuchi:
Multiobjective data mining from solutions by evolutionary multiobjective optimization. GECCO 2017: 617-624 - [c150]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 - [c149]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 - [c148]Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Performance comparison of EMO algorithms on test problems with different search space shape. IFSA-SCIS 2017: 1-6 - [c147]Ken Doi, Ryo Imada, Yusuke Nojima, Hisao Ishibuchi:
Use of Inverted Triangular Weight Vectors in Decomposition-Based Many-Objective Algorithms. SEAL 2017: 321-333 - [c146]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) - [c145]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 - [c144]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
Sensitivity of performance evaluation results by inverted generational distance to reference points. CEC 2016: 1107-1114 - [c143]Hisao Ishibuchi, Ken Doi, Yusuke Nojima:
Characteristics of many-objective test problems and penalty parameter specification in MOEA/D. CEC 2016: 1115-1122 - [c142]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 - [c141]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 - [c140]Yuki Tanigaki, Yusuke Nojima, Hisao Ishibuchi:
Meta-optimization based multi-objective test problem generation using WFG toolkit. CEC 2016: 2768-2775 - [c139]Hiroyuki Masuda, Yusuke Nojima, Hisao Ishibuchi:
Common properties of scalable multiobjective problems and a new framework of test problems. CEC 2016: 3011-3018 - [c138]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 - [c137]Yusuke Nojima, Hisao Ishibuchi:
Multiobjective fuzzy genetics-based machine learning with a reject option. FUZZ-IEEE 2016: 1405-1412 - [c136]Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, Yusuke Nojima:
Weighted Optimization Framework for Large-scale Multi-objective Optimization. GECCO (Companion) 2016: 83-84 - [c135]Hisao Ishibuchi, Ken Doi, Yusuke Nojima:
Use of Piecewise Linear and Nonlinear Scalarizing Functions in MOEA/D. PPSN 2016: 503-513 - [c134]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 - [c133]Hisao Ishibuchi, Ken Doi, Yusuke Nojima:
Reference point specification in MOEA/D for multi-objective and many-objective problems. SMC 2016: 4015-4020 - [c132]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) - [c131]Yusuke Nojima, Yuji Takahashi, Hisao Ishibuchi:
Application of Parallel Distributed Implementation to Multiobjective Fuzzy Genetics-Based Machine Learning. ACIIDS (1) 2015: 462-471 - [c130]Yuji Takahashi, Yusuke Nojima, Hisao Ishibuchi:
Rotation effects of objective functions in parallel distributed multiobjective fuzzy genetics-based machine learning. ASCC 2015: 1-6 - [c129]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 - [c128]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 - [c127]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
Comparing solution sets of different size in evolutionary many-objective optimization. CEC 2015: 2859-2866 - [c126]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 - [c125]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 - [c124]Hisao Ishibuchi, Hiroyuki Masuda, Yuki Tanigaki, Yusuke Nojima:
Modified Distance Calculation in Generational Distance and Inverted Generational Distance. EMO (2) 2015: 110-125 - [c123]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 - [c122]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 - [c121]Hisao Ishibuchi, Hiroyuki Masuda, Yusuke Nojima:
A Study on Performance Evaluation Ability of a Modified Inverted Generational Distance Indicator. GECCO 2015: 695-702 - [c120]Hisao Ishibuchi, Ken Doi, Hiroyuki Masuda, Yusuke Nojima:
Relation Between Weight Vectors and Solutions in MOEA/D. SSCI 2015: 861-868 - [c119]Yusuke Nojima, Chun-Hao Chen:
Genetic fuzzy systems and its application to data mining. TAAI 2015: 33 - [c118]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) - [c117]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 - [c116]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 - [c115]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 - [c114]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 - [c113]Yuji Takahashi, Yusuke Nojima, Hisao Ishibuchi:
Hybrid fuzzy genetics-based machine learning with entropy-based inhomogeneous interval discretization. FUZZ-IEEE 2014: 1512-1517 - [c112]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 - [c111]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 - [c110]Hisao Ishibuchi, Yuki Tanigaki, Hiroyuki Masuda, Yusuke Nojima:
Distance-Based Analysis of Crossover Operators for Many-Objective Knapsack Problems. PPSN 2014: 600-610 - [c109]Yuki Tanigaki, Kaname Narukawa, Yusuke Nojima, Hisao Ishibuchi:
Preference-based NSGA-II for many-objective knapsack problems. SCIS&ISIS 2014: 637-642 - [c108]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 - [c107]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) - [c106]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. BigData 2013: 63-70 - [c105]Yusuke Nojima, Xian-Hua Han, Kazuki Taniguchi, Yen-Wei Chen:
High frequency compensated face hallucination with total variation constraint. BMEI 2013: 831-835 - [c104]Hisao Ishibuchi, Takahiko Sudo, Koichiro Hoshino, Yusuke Nojima:
Evolution of cooperative strategies for iterated prisoner's dilemma on networks. CASoN 2013: 32-37 - [c103]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 - [c102]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 - [c101]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 - [c100]Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima:
Difficulty in Evolutionary Multiobjective Optimization of Discrete Objective Functions with Different Granularities. EMO 2013: 230-245 - [c99]Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima:
Relation between Neighborhood Size and MOEA/D Performance on Many-Objective Problems. EMO 2013: 459-474 - [c98]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 - [c97]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 - [c96]Yusuke Nojima, Hisao Ishibuchi:
Multiobjective genetic fuzzy rule selection with fuzzy relational rules. GEFS 2013: 60-67 - [c95]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 - [c94]Hisao Ishibuchi, Koichiro Hoshino, Yusuke Nojima:
Neighborhood Specification for Game Strategy Evolution in a Spatial Iterated Prisoner's Dilemma Game. LION 2013: 215-230 - [c93]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 - [c92]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) - [c91]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 - [c90]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 - [c89]