Portrait of Yusuke Nojima, Professor at Osaka Metropolitan University

Yusuke Nojima

Professor and Vice Dean

Research fields: computational intelligence, fuzzy systems, evolutionary computation, multiobjective optimization, evolutionary machine learning, fuzzy genetics-based machine learning, explainable AI, interpretable AI, fairness-aware AI, and reliable AI.

Yusuke Nojima is a Professor in the Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Japan. He leads research on human-centered and interpretable computational intelligence, including fuzzy rule-based classifiers, multiobjective evolutionary fuzzy systems, quality-diversity algorithms, and AI methods for transparent and reliable decision support.

Computational Intelligence Laboratory
Dept. of Core Informatics
Graduate School of Informatics
Osaka Metropolitan University

E-mail: nojima (at) omu.ac.jp
Address: 1-1 Gakuen-cho, Sakai, Osaka 599-8531, Japan
Phone: +81-72-254-9350
FAX: +81-72-254-9825

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Research Profile

Yusuke Nojima's research covers computational intelligence and soft computing, especially fuzzy systems, genetic fuzzy systems, fuzzy rule-based classifiers, evolutionary computation, multiobjective optimization, many-objective optimization, multimodal multiobjective optimization, evolutionary machine learning, data mining, explainable artificial intelligence, interpretable machine learning, fairness-aware AI, and reliable AI.

Representative topics include multiobjective fuzzy genetics-based machine learning, interpretable and explainable fuzzy classifiers, accuracy-interpretability trade-offs, accuracy-fairness trade-offs, quality-diversity algorithms such as MAP-Elites, MOEA/D and reference-vector adaptation, constrained multiobjective optimization, continual learning, adaptive resonance theory-based clustering, and real-world optimization problems.

New Info



Biography



Yusuke Nojima received the B.S. and M.S. Degrees in mechanical engineering from Osaka Institute of Technology, Osaka, Japan, in 1999 and 2001, respectively, and the Ph.D. degree from the Department of System Function Science from Kobe University, Hyogo, Japan, in 2004. Since 2004, he has been with Osaka Prefecture University, Osaka, Japan, where he was a Professor in the Department of Computer Science and Intelligent Systems from October 2020. From April 2022, he has been a Professor in the Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University. His research interests include evolutionary fuzzy systems, evolutionary multiobjective optimization, and multiobjective data mining. He was a guest editor for several special issues in international journals. He was a task force chair on Evolutionary Fuzzy Systems in Fuzzy Systems Technical Committee of IEEE Computational Intelligence Society. He was an associate editor of IEEE Computational Intelligence Magazine (2014-2019).


2004.4-2007.3: Research Associate: Osaka Prefecture University, JAPAN
2007.4-2013.3: Assistant Professor: Osaka Prefecture University, JAPAN
2013.4-2020.9: Associate Professor: Osaka Prefecture University, JAPAN
2020.10-2022.3: Professor: Osaka Prefecture University, JAPAN
2022.4- : Professor: Osaka Metropolitan University, JAPAN
2026.4- : Vice Dean: Graduate School of Informatics, Osaka Metropolitan University, JAPAN

Awards
Research Stay

Publications



Edited Book
  1. M. A. Mellal, Y. Nojima, and N. Masuyama, Computational Intelligence for a Greener Future: Innovations in Renewable Energy Systems: Integrating AI and Behavioral Insights to Drive Economic Efficiency and Sustainability, Studies in Computational Intelligence 1240, 540 pages, Springer, January 2026.

Book Chapters
  1. K. van der Blom, T. M. Deist, V. Volz, M. Marchi, Y. Nojima, B. Naujoks, A. Oyama, and T. Tušar, "Identifying properties of real-world optimisation problems through a questionnaire," Many-Criteria Optimization and Decision Analysis: State-of-the-Art, Present Challenges, and Future Perspectives, springer, pp. 59-80, July 2023.
  2. H. Ishibuchi and Y. Nojima "Multiobjective Genetic Fuzzy Systems," In J. Kacprzyk and W. Pedrycz (eds.) Springer Handbook of Computational Intelligence, Springer-Verlag Berlin Heidelberg, pp. 1479-1498 (Chapter 77), May 2015.
  3. Y. Nojima, S. Mihara, and H. Ishibuchi, "Parallel Distributed Genetic Rule Selection for Data Mining from Large Data Sets," In F. Kojima, F. Kobayashi, and H. Nakamoto (eds.) Simulation and Modeling Related to Computational Science and Robotics Technology, IOS Press, Amsterdam, Netherlands, pp. 140-154, August 2012.
  4. H. Ishibuchi and Y. Nojima, "Multiobjective Genetic Fuzzy Systems," In C. L. Mumford and L. C. Jain (eds.): Computational Intelligence: Collaboration, Fusion and Emergence, Springer, Berlin, pp. 131-173 (Chapter 5), July 2009.
  5. H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, "Implementation of Multiobjective Memetic Algorithms for Combinatorial Optimization Problems: A Knapsack Problem Case Study," In C. K. Goh, Y. S. Ong, and K. C. Tan (eds.): Multi-Objective Memetic Algorithms, Springer, Berlin, pp. 27-49 (Chapter 2), February 2009.
  6. H. Ishibuchi, Y. Nojima, and I. Kuwajima, "Evolutionary Multiobjective Design of Fuzzy Rule-based Classifiers," In J. Fulcher and L. C. Jain (eds.): Computational Intelligence: A Compendium, Springer, Berlin, pp. 641-685 (Chapter 15), July 2008.
  7. H. Ishibuchi, I. Kuwajima, and Y. Nojima, "Evolutionary Multiobjective Rule Selection for Classification Rule Mining," In A. Ghosh, K. S. Dehuri, and S. Ghosh (eds.): Multi-objective Evolutionary Algorithms for Knowledge Discovery from Databases, Springer, Berlin, pp. 47-70 (Chapter 3), March 2008.
  8. H. Ishibuchi, I. Kuwajima, and Y. Nojima, "Multiobjective Classification Rule Mining," In J. Knowles, D. Corne, and K. Deb (eds.): Multiobjective Problem Solving from Nature: From Concepts to Applications, Springer, Berlin, pp. 219-240, January 2008.
  9. H. Ishibuchi and Y. Nojima, "Pattern Classification with Linguistic Rules," In H. Bustince, F. Herrera, and J. Montero (eds.): Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Intelligent Systems from Decision Making to Data Mining, Web Intelligence and Computer Vision, Springer, Berlin, pp. 377-395, November 2007.
  10. H. Ishibuchi, I. Kuwajima, and Y. Nojima, "Use of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection," In P. Melin, O. Castillo, E. G. Ramirez, J. Kacprzyk and W. Pedrycz (ed.): Analysis and Design of Intelligent Systems using Soft Computing Techniques (Advances in Soft Computing 41), Springer, Berlin, pp. 387-396, 2007.
  11. H. Ishibuchi, Y. Nojima, "Fuzzy Ensemble Design through Multiobjective Fuzzy Rule Selection," In Y. Jin (ed.): Multi-Objective Machine Learning, Springer, Berlin, pp. 507-530, 2006.
  12. Y. Nojima, F. Kojima, N. Kubota, "Local Episode-based Learning of a Mobile Robot in a Dynamic Environment," Dynamic Systems Approach for Embodiment and Sociality -From Ecological Psychology to Robotics-, International Series on Advanced Intelligence Vol. 6, pp. 318-322, 2003.

Journal Papers
  1. S. Zhai, R. Jiao, B. Xue, Y. Nojima, and M. Zhang, "MILE: Multi-expert ensemble with instance selection for multi-class imbalanced learning," IEEE Transactions on Evolutionary Computation, (Early Access)
  2. R. Jiao, N. Masuyama, and Y. Nojima, "Class-imbalanced learning via multi-task multi-objective feature selection," IEEE Transactions on Artificial Intelligence, (Early Access)
  3. J. M. Aragon-Jurado, J. C. del la Torre, Y. Nojima, P. Ruiz, and B. Dorronsoro, "Two-level automatic software optimization using cooperative co-evolutionary algorithms," Applied Soft Computing, vol. 195, Article ID 115066, 17 pages, June 2026.
  4. N. Masuyama, T. Takebayashi, Y. Nojima, C. K. Loo, H. Ishibuchi, and S. Wermter, "A parameter-free adaptive resonance theory-based topological clustering algorithm capable of continual learning," Neural Computing and Applications, vol. 38, Article ID 217, 31 pages, March 2026.
  5. T. Konishi, N. Masuyama, J. Casillas, and Y. Nojima, "Fairness via fuzzy systems: Analysis of accuracy–fairness tradeoff by multiobjective fuzzy genetics-based machine learning," IEEE Transactions on Fuzzy Systems, vol. 34, no. 2, pp. 650-664, Dec. 2025.
  6. Y. Li, F.-l. Chung, Y. Nojima, and S. Wang, "Fully interpretable Gaussian centralized TSK fuzzy classifier from probabilistic perspective: Concepts, output-stability-based learning, and ensemble," IEEE Transactions on Fuzzy Systems, vol. 33, no. 11, pp. 3967-3980, Nov. 2025.
  7. Y. Li, F.-l. Chung, Y. Nojima, and S. Wang, "A calibrated fully interpretable fuzzy classifier via Vapnik–Chervonenkis-dimension minimization learning," Information Sciences, vol. 713, Article ID 122142, September 2025.
  8. N. Masuyama, Y. Nojima, Y. Toda, C. K. Loo, H. Ishibuchi, and N. Kubota, "Privacy-preserving continual federated clustering via adaptive resonance theory," IEEE Access, vol. 12, pp. 139692-139710, 2024.
  9. N. Bayuaji, Kurnianingsih, N. Masuyama, and Y. Nojima, "CNN-LSTM for heartbeat sound classification," International Journal on Informatics Visualization, vol. 8, no. 2, June 2024.
  10. E. Zhou, C. -M. Vong, Y. Nojima, and S. Wang, "Internally and generatively decorrelated ensemble of first-order Takagi–Sugeno–Kang fuzzy regressors with quintuply diversity guarantee,"IEEE Transactions on Fuzzy Systems, vol. 32, no. 3, pp. 1288-1302, March 2024.
  11. Y. Wang, Y. Wang, Y. Han, J. Li, K. Gao, and Y. Nojima, "Intelligent optimization under multiple factories: Hybrid flow shop scheduling problem with blocking constraints using an advanced iterated greedy algorithm," Complex System Modeling and Simulation, vol. 3, no. 4, pp. 282-306, Dec. 2023.
  12. T. Kinoshita, N. Masuyama, Y. Liu, Y. Nojima, and H. Ishibuchi, "Reference vector adaptation and mating selection strategy via adaptive resonance theory-based clustering for many-objective optimization," IEEE Access, vol. 11, pp. 126066-126086, Nov. 2023.
  13. N. Masuyama, Y. Nojima, F. Dawood, and Z. Liu, "Class-wise classifier design capable of continual learning using adaptive resonance theory-based topological clustering," Applied Sciences, vol. 13, no. 21, 11980, Nov. 2023.
  14. Y. Xu, C. Xu, H. Zhang, L. Huang, Y. Liu, Y. Nojima, and X. 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 Transactions on Cybernetics, vol. 53, no. 11, pp. 6998-7007, Nov. 2023.
  15. Z. Bian, J. Zhang, Y. Nojima, F.-l. Chung, and S. Wang, "Hybrid-ensemble-based interpretable TSK fuzzy classifier for imbalanced data," Information Fusion, vol. 98, 101845, Oct. 2023.
  16. N. Masuyama, Y. Nojima, C. K. Loo, and H. Ishibuchi, "Multi-label classification via adaptive resonance theory-based clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 7, pp. 8696-8712, July 2023.
  17. Y. Xu, H. Zhang, L. Huang, R. Qu, and Y. Nojima, "A Pareto Front grid guided multi-objective evolutionary algorithm," Applied Soft Computing, vol. 136, article ID, 110095, 19 pages, March 2023.
  18. M. A. Mellal, E. Zio, S. Al-Dahidi, N. Masuyama, and Y. Nojima, "System design optimization with mixed subsystems failure dependencies," Reliability Engineering & System Safety, vol. 231, article ID, 109005, 12 pages, March 2023.
  19. Y. Okazaki, Y. Fujita, H. Murata, N. Masuyama, Y. Nojima, H. Ikeno, S. Yagi, and I. Yamada, "Composition-designed multielement perovskite oxides for oxygen evolution catalysis," Chemistry of Materials, vol. 34, no. 24, pp. 10973–10981, Dec. 2022.
  20. Y. Xu, H. Zhang, X. Zeng, and Y. Nojima, "An adaptive convergence enhanced evolutionary algorithm for many-objective optimization problems," Swarm and Evolutionary Computation, vol. 75, 101180, Dec. 2022.
  21. E. Zhou, C. M. Vong, Y. Nojima, and S. Wang, "A fully interpretable first-order TSK fuzzy system and its training with negative entropic and rule-stability-based regularization," IEEE Transactions on Fuzzy Systems, vol. 31, no. 7, pp. 2305-2319, Nov. 2022.
  22. B. Qin, F.-l. Chung, Y. Nojima, H. Ishibuchi, and S. Wang, "Fuzzy rule dropout with dynamic compensation for wide learning algorithm of TSK fuzzy classifier," Applied Soft Computing, vol. 127, 14 pages, September 2022.
  23. N. Masuyama, N. Amako, Y. Yamada, Y. Nojima, and H. Ishibuchi, "Adaptive resonance theory-based topological clustering with a divisive hierarchical structure capable of continual learning," IEEE Access, vol. 10, pp. 68042-68056, June 2022.
  24. X. Zhang, Y. Nojima, H. Ishibuchi, W. Hu, and S. Wang, “Prediction by fuzzy clustering and KNN on validation data with parallel ensemble of interpretable TSK fuzzy classifiers,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 1, pp. 400-414, Jan. 2022.
  25. B. Qin, Y. Nojima, H. Ishibuchi, and S. Wang, "Realizing deep high-order TSK fuzzy classifier by ensembling interpretable zero-order TSK fuzzy subclassifiers," IEEE Trans. on Fuzzy Systems, vol. 29, no. 11, pp. 3441-3455, Nov. 2021.
  26. X. Han, Y. Han, Q. Chen, J. Li, H. Sang, Y. Liu, Q. Pan, and Y. Nojima, "Distributed flow shop scheduling with sequence-dependent setup times using an improved iterated greedy algorithm," Complex System Modeling and Simulation, vol. 1, no. 3, pp. 198-217, Sep. 2021.
  27. G. Suhang, Y. Nojima, H. Ishibuchi, and S. Wang, “Fuzzy style k-plane clustering,” IEEE Trans. on Fuzzy Systems, vol. 29, no. 6, pp. 1518-1532, June 2021.
  28. Y. Xu, C. Yang, S. Peng and Y. Nojima, "A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning," Applied Intelligence, vol. 50, pp. 3852-3867, July 2020.
  29. C.-H. Chen, H. Chou, T.-P. Hong, and Y. Nojima, “Cluster-based membership function acquisition approaches for mining fuzzy temporal association rules,” IEEE Access, vol. 8, pp. 123996-124006, June 2020.
  30. Y. Liu, H. Ishibuchi, G. G. Yen, Y. Nojima, and N. Masuyama, “Handling imbalance between convergence and diversity in the decision space in evolutionary multi-modal multi-objective optimization,” IEEE Trans. on Evolutionary Computation, vol. 24, no. 3, pp. 551-565, June 2020.
  31. Y. Liu, H. Ishibuchi, N. Masuyama, and Y. Nojima, "Adapting reference vectors and scalarizing functions by growing neural gas to handle irregular Pareto fronts," IEEE Transactions on Evolutionary Computation, vol. 24, no. 3, pp. 439-453, June 2020.
  32. G. Suhang, Y. Nojima, H. Ishibuchi, S. Wang, “A novel classification method from the perspective of fuzzy social networks based on physical and implicit style features of data,” IEEE Trans. on Fuzzy Systems, vol. 28, no. 2, pp. 361-375, Feb. 2020.
  33. N. Masuyama, C. K. Loo, H. Ishibuchi, N. Kubota, Y. Nojima, and Y. Liu, "Topological clustering via adaptive resonance theory with information theoretic learning," IEEE Access, vol. 7, no. 1, pp. 76920-76936, December 2019.
  34. Y. Tanigaki, N. Masuyama, and Y. Nojima, “Effect of the number of constraints on the performance of multi-objective evolutionary algorithms,” International Journal of Computer Science and Network Security, vol. 18, no.12, pp. 221-231, December 2018.
  35. H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Reference point specification in inverted generational distance for triangular linear Pareto front,” IEEE Trans. on Evolutionary Computation, vol. 22, no. 6, pp. 961-975, December 2018.
  36. H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “How to specify a reference point in hypervolume calculation for fair performance comparison,” Evolutionary Computation, vol. 26, no. 3, pp. 411-440, Fall 2018. PDF
  37. H. Zille, H. Ishibuchi, S. Mostaghim, and Y. Nojima, “A framework for large-scale multi-objective optimization based on problem transformation,” IEEE Trans. on Evolutionary Computation, vol. 22, no. 2, pp. 260-274, April 2018.
  38. H. Ishibuchi, K. Doi, and Y. Nojima, “On the effect of normalization in MOEA/D for multi-objective and many-objective optimization,” Complex & Intelligent Systems, vol. 3, pp. 279–294, Dec 2017.
  39. J. Alcala-Fdez, R. Alcala, S. Gonzalez, Y. Nojima, and S. Garcia, "Evolutionary fuzzy rule-based methods for monotonic classification," IEEE Trans. on Fuzzy Systems, vol. 25, no. 6, pp. 1376-1390, June 2017.
  40. C.-H. Chen, C.-C. Chen, and Y. Nojima, "An efficient and effective approach for mining a group stock portfolio using MapReduce," Intelligent Data Analysis, vol. 21, no. S1, pp. S217-S232, 2017.
  41. 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, vol. 21, no.2, pp. 169-190, April 2017. 2020 IEEE TEVC Outstanding Paper Award
  42. 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.
  43. 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, Total 29 pages. February 2016. (Online Journal)
  44. 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.
  45. 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.
  46. 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. PDF
  47. H. Ishibuchi, S. Mihara, and Y. Nojima, "Parallel distributed hybrid fuzzy GBML models with rule set migration and training data rotation," IEEE Transactions on Fuzzy Systems, vol. 21, no. 2, pp. 355-368, April 2013. Open Access!
  48. M. Fazzolari, R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, "A review of the application of multiobjective evolutionary fuzzy systems: current status and future directions," IEEE Transactions on Fuzzy Systems, vol. 21, no. 1, pp. 45-65, February 2013.
  49. H. Ishibuchi, Y. Nakashima, and Y. Nojima, "Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning," Soft Computing, vol. 15, no. 12, pp. 2415-2434, November 2011. [PDF]
  50. R. Alcala, Y. Nojima, F. Herrera, and H. Ishibuchi, "Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions," Soft Computing, vol. 15, no. 12, pp. 2303-2318, November 2011.
  51. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization," Soft Computing, vol. 15, no. 9, pp. 1749-1767, September 2011.
  52. H. Ishibuchi, H. Ohyanagi, and Y. Nojima, "Evolution of strategies with different representation schemes in a spatial iterated prisoner's dilemma game," IEEE Trans. on Computational Intelligence and AI in Games, vol. 3, no. 1, pp. 67-82, March 2011.
  53. H. Ishibuchi, Y. Kaisho, and Y. Nojima, "Design of linguistically interpretable fuzzy rule-based classifiers: A short review and open questions," Journal of Multiple-Valued Logic and Soft Computing, vol. 17, no. 2-3, pp. 101-134, 2011.
  54. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Diversity improvement by non-geometric binary crossover in evolutionary multiobjective optimization," IEEE Transactions on Evolutionary Computation, vol. 14, no. 6, pp. 985-998, December 2010.
  55. Y. Nojima, Y. Hamada, and H. Ishibuchi, "Application of interactive fuzzy data mining to the analysis of inter-vehicle communication in traffic simulations," ICGST International Journal on Automation, Robotics and Autonomous Systems, vol. 9, no. 2, pp. 17-25, December 2009.
  56. Y. Nojima and H. Ishibuchi, "Incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems," Artificial Life and Robotics, vol. 14, no. 3, pp. 418-421, December 2009. [PDF]
  57. Y. Hamada, Y. Nojima and H. Ishibuchi, "Use of multi-objective genetic rule selection for examining the effectiveness of inter-vehicle communication in traffic simulations," Artificial Life and Robotics, vol. 14, no. 3, pp. 410-413, December 2009.
  58. H. Ohyanagi, Y. Wakamatsu, Y. Nakashima, Y. Nojima and H. Ishibuchi, "Evolution of cooperative behavior among heterogeneous agents with different strategy representations in an iterated prisoner's dilemma," Artificial Life and Robotics, vol. 14, no. 3, pp. 414-417, December 2009.
  59. H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, "Use of biased neighborhood structures in multi-objective memetic algorithms," Soft Computing, vol. 13, no. 8-9, pp. 795-810, July 2009.
  60. Y. Nojima, H. Ishibuchi, and I. Kuwajima, "Parallel distributed genetic fuzzy rule selection," Soft Computing, vol. 13, no. 5, pp. 511-519, March 2009. [PDF]
  61. N. Tsukamoto, Y. Nojima, and H. Ishibuchi, "Effects of non-geometric binary crossover on multiobjective 0/1 knapsack problems," Artificial Life and Robotics, vol. 13, no. 2, pp. 434-437, February 2009.
  62. I. Kuwajima, Y. Nojima, and H. Ishibuchi, "Effects of constructing fuzzy discretization from crisp discretization for rule-based classifiers," Artificial Life and Robotics, vol. 13, no. 1, pp. 294-297, December 2008.
  63. I. Kuwajima, Y. Nojima, and H. Ishibuchi, "Obtaining accurate classifiers with Pareto-optimal and near Pareto-optimal rules," Artificial Life and Robotics, vol. 13, no. 1, pp. 315-319, December 2008.
  64. H. Ishibuchi. K. Narukawa, N. Tsukamoto, and Y. Nojima, "An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization," European Journal of Operational Research, vol. 188, no. 1, pp. 57-75, July 2008.
  65. Y. Nojima and H. Ishibuchi, "Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design," International Journal of Hybrid Intelligent Systems, vol. 4, no. 3, pp. 157-169, October 2007. [PDF]
  66. H. Ishibuchi and Y. Nojima, "Analysis of interpretability-accuracy tradeoff by multiobjective fuzzy genetics-based machine learning," International Journal of Approximate Reasoning, Vol. 44, No. 1, pp. 4-31, January 2007.
  67. H. Ishibuchi and Y. Nojima, "Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers," International Journal of Hybrid Intelligent Systems, Vol. 3, No. 3, pp. 129-145, 2006. [PDF]
  68. N. Kubota, Y. Nojima, F. Kojima, and T. Fukuda, "Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot," Soft Computing, Vol. 10, No. 10, pp. 891-901, 2006.
  69. H. Ishibuchi, K. Narukawa, and Y. Nojima, "Handling of overlapping objective vectors in evolutionary multiobjective optimization," International Journal of Computational Intelligence Research, Vol. 1, No. 1, pp. 1-18, 2005.
  70. N. Kubota, Y. Nojima, F. Kojima, T. Fukuda, and S. Shibata, "Path planning and control for a flexible transfer system," Journal of Robotics & Mechatronics, Vol.12, No.2, pp. 103-109, April 2000.

Conference Papers

    2026
  1. Y. Azuma, N. Masuyama, and Y. Nojima, "A priority-aware constraint handling framework using round-robin selection for constrained multi-objective optimization," in Proc. of 2026 IEEE Congress on Evolutionary Computation (IEEE CEC 2026), 6 pages, Maastricht, Netherlands, June 21-26, 2026.
  2. T. Tokusaka, N. Masuyama, and Y. Nojima, "A decomposition-based constrained multimodal multiobjective evolutionary algorithm with constraint-tightening three-objective subproblems," in Proc. of 2026 IEEE Congress on Evolutionary Computation (IEEE CEC 2026), 6 pages, Maastricht, Netherlands, June 21-26, 2026.
  3. T. Konishi, N. Masuyama, and Y. Nojima, "Improving individual fairness in fuzzy classifiers via evolutionary multi-objective optimization," in Proc. of Proc. of 2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2026), 6 pages, Maastricht, Netherlands, June 21-26, 2026.
  4. Y. Shuto, N. Masuyama, and Y. Nojima, "Improving generalization performance of multi-objective fuzzy genetics-based machine learning using synthetic training data generated from high-performance black-box models," in Proc. of 2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2026), 6 pages, Maastricht, Netherlands, June 21-26, 2026.
  5. C.-S. Lee, M.-H. Wang, C.-C. Yue, S.-C. Yang, Y. Nojima, N. Masuyama, T. Konishi, and R. Saga, "Fuzzy inference–driven knowledge modeling for LLM-assisted human–machine co-learning," in Proc. of 2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2026), 6 pages, Maastricht, Netherlands, June 21-26, 2026.
  6. S. Inoue, N. Masuyama, Y. Nojima, Y. Toda, Z. Liu, C. K. Loo, and W. S. Liew, "Cluster merging in adaptive resonance theory-based clustering guided by mass and distance criteria," in Proc. of 2026 International Joint Conference on Neural Networks (IJCNN2026), 6 pages, Maastricht, Netherlands, June 21-26, 2026.

    7. 2025
  7. R. Ono, N. Masuyama, and Y. Nojima, "Effects of clustering-based under-sampling methods on multi-objective knowledge acquisition from constrained multiobjective problems," in Proc. of the 26th International Symposium on Advanced Intelligent Systems, 6 pages, Cheongju, Korea, Nov. 6-9, 2025.
  8. R. Meneust, N. Masuyama, and Y. Nojima, "Fuzzy classifier counterfactual explanation generation," in Proc. of the 2025 International Conference on Fuzzy Theory and Its Applications (iFuzzy2025), 2 pages, Hsinchu, Taiwan, Nov. 5-8, 2025.
  9. Y. Nojima, R. Ono, and N. Masuyama, "Fuzzy rule-based knowledge acquisition from solutions of constrained problems," in Proc. of 2025 International Fuzzy Systems Association (IFSA) World Congress and 2025 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), 2 pages, Alberta, Canada, Aug. 16-19, 2025.
  10. L. Pickering, Y. Nojima, K. Cohen, and B. De Baets, "Using the lenses of interpretability for analyzing FRBMs trained on electronic health records," in Proc. of 2025 International Fuzzy Systems Association (IFSA) World Congress and 2025 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), 6 pages, Alberta, Canada, Aug. 16-19, 2025.
  11. Y. Nojima, A. Tomofuji, E. Vernon, and N. Masuyama, "Partial Interpretable Models using a Fuzzy Classifier and Random Forest," in Proc. of 2025 International Fuzzy Systems Association (IFSA) World Congress and 2025 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), 6 pages, Alberta, Canada, Aug. 16-19, 2025.
  12. C.-S. Lee, M.-H. Wang, C.-C. Yue, G.-Y. Tseng, and Y. Nojima, "Fuzzy estimation agent with knowledge graph and quantum fuzzy inference engine for Taiwanese-English co-learning," in Proc. of 2025 International Fuzzy Systems Association (IFSA) World Congress and 2025 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), 12 pages, Alberta, Canada, Aug. 16-19, 2025.
  13. R. Jiao, T. Peng, N. Masuyama, and Y. Nojima, "LIMES: Learning from Imbalanced Data via Multiform Multi-Objective Feature Selection," Proc. of the Genetic and Evolutionary Computation Conference Companion, pp. 287-290, Malaga, Spain, July 14-18, 2025.
  14. T. Konishi, N. Masuyama, and Y. Nojima, "MEFC: Map-elites-based fuzzy classifier design," in Proc. of the 2025 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-7, Reims, France, July 6-10, 2025.
  15. N. Masuyama, R. Fujii, T. Kinoshita, and Y. Nojima, "A clustering-based sample selection method for improving replay buffer quality in continual self-supervised learning," in Proc. of the 2025 International Joint Conference on Neural Networks (IJCNN), pp. 1-7, Roma, Italy, June 30 - July 5, 2025.

    16. 2024
  16. T. Tokusaka, N. Masuyama, and Y. Nojima, "Constrained multi-modal multi-objective evolutionary algorithm with problem transformation into two-objective subproblems," in Proc. of the 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2024), 6 pages, Himeji, Japan, November 9 - 12, 2024.
  17. A. K. Tabouret, E. M. Vernon, N. Masuyama, and Y. Nojima, "Enhancing machine learning interpretability through a graphical user interface," in Proc. of the 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2024), 5 pages, Himeji, Japan, November 9 - 12, 2024.
  18. Y. Azuma, N. Masuyama, and Y. Nojima, "A decomposition-based constrained multi-objective evolutionary algorithm with adaptive weight adjustment," in Proc. of the 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2024), 5 pages, Himeji, Japan, November 9 - 12, 2024.
  19. T. Takebayashi, N. Masuyama, and Y. Nojima, "Clustering-based automatic codeword length determination in self-supervised learning," in Proc. of the 2024 International Conference on Machine Learning and Cybernetics (ICMLC), pp. 1-6, Miyazaki, Japan, September 20-23, 2024.
  20. Y. Nojima, S. Takasaki, S. Fukuda, and N. Masuyama "Importance of temporal information in fish habitat assessment using multiobjective fuzzy genetics-based machine learning," in Proc. of 2024 International Conference on Fuzzy Theory and Its Applications (iFuzzy 2024), Kagawa, Japan, Aug. 10-13, 2024. Best Presentation Paper Award
  21. K. Tashiro, N. Masuyama, and Y. Nojima, "A growing hierarchical clustering algorithm via parameter-free adaptive resonance theory," in Proc. of 2024 International Joint Conference on Neural Networks (IJCNN 2024), Yokohama, Japan, pp. 1-6, June 30-July 5, 2024.
  22. T. Kinoshita, N. Masuyama, and Y. Nojima, "A federated data-driven multiobjective evolutionary algorithm via continual learnable clustering," in Proc. of 2024 IEEE Congress on Evolutionary Computation (CEC 2024), Yokohama, Japan, pp. 1-7, June 30-July 5, 2024.
  23. T. Konishi, N. Masuyama, J. Casillas, and Y. Nojima, "Fairness-aware classifier design via multi-objective fuzzy genetics-based machine learning," in Proc. of 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2024), Yokohama, Japan, pp. 1-8, June 30-July 5, 2024.
  24. R. Fuerst, N. Masuyama, and Y. Nojima, "Hierarchical fuzzy classifier design using a reject option," in Proc. of 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2024), Yokohama, Japan, pp. 1-7, June 30-July 5, 2024.
  25. C.-S. Lee, M.-H. Wang, J.-K. Chiang, N. Kubota, E. S. Shimokawara, Y. Nojima, G. Acampora, P.-Y. Wu, S.-C. Chiu, S.-C. Yang, and C.-Z. Siow, "Quantum computational intelligence with generative AI image for human-machine interaction," in Proc. of 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2024), Yokohama, Japan, pp. 1-8, June 30-July 5, 2024.
  26. E. M. Vernon, N. Masuyama, and Y. Nojima, "Integrating white and black box techniques for interpretable machine learning," in Proc. of the 9th International Congress on Information and Communication Technology (ICICT), 10 pages, London, UK (Hybrid), February 19-22, 2024. [Arxiv]

    27. 2023
  27. T. Kinoshita, N. Masuyama, and Y. Nojima, "Riesz s-energy indicators for diversity assessment of multiobjective evolutionary algorithms," in Proc. of 24th International Symposium on Intelligent Systems (ISIS 2023), 7 pages, Gwangju, Korea, December 6-9, 2023.
  28. Y. Nojima, T. Tokusaka, and N. Masuyama, "Effects of parent selection schemes on the search performance of multi-modal multi-objective evolutionary algorithm with problem transformation into two-objective subproblems," in Proc. of 24th International Symposium on Intelligent Systems (ISIS 2023), 6 pages, Gwangju, Korea, December 6-9, 2023.
  29. T. Konishi, N. Masuyama, and Y. Nojima, "Effects of complexity enhancements on the search performance of multiobjective fuzzy genetics-based machine learning," in Proc. of the 20th World Congress of the International Fuzzy Systems Association (IFSA), pp. 38-45, Daegu, Korea, Aug. 20-23, 2023. Best Paper Award
  30. Y. Nojima, K. Kawano, H. Shimahara, E. Vernon, N. Masuyama, and H. Ishibuchi "Fuzzy Classifiers with a two-stage reject option," in Proc. of 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-6, Songdo Incheon, Korea, Aug. 13-17, 2023.
  31. Y. Nojima, Y. Fujii, N. Masuyama, Y. Liu, and H. Ishibuchi, "A decomposition-based multi-modal multi-objective evolutionary algorithm with problem transformation into two-objective subproblems," in Proc. of the Companion Conference on Genetic and Evolutionary Computation (GECCO), pp. 399–402, Lisboa, Portugal, July 15-19, 2023.

    32. 2022
  32. T. Konishi, N. Masuyama, and Y. Nojima, "Effects of accuracy-based single-objective optimization in multiobjective fuzzy genetics-based machine learning," Proc. of 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS2022), 6 pages, Mie, Japan, November 29 - December 2, 2022.
  33. T. Kinoshita, N. Masuyama, and Y. Nojima, "Search process analysis of multiobjective evolutionary algorithms using convergence-diversity diagram," Proc. of 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS2022), 6 pages, Mie, Japan, November 29 - December 2, 2022. (T. Kinoshita recieved IEEE CIS Japan Chapter Young Researcher Award)
  34. E. M. Vernon, N. Masuyama, and Y. Nojima, "Error-reject tradeoff analysis on two-stage classifier design with a reject option," Proc. of World Automation Congress (WAC 2022), pp. 312-317, Texas, USA, October 11-15, 2022.
  35. M. Yano, N. Masuyama, and Y. Nojima, "Behavior analysis of constrained multiobjective evolutionary algorithms using scalable constrained multi-modal distance minimization problems," Proc. of World Automation Congress (WAC 2022), pp. 174-179, Texas, USA, October 11-15, 2022.
  36. T. Kinoshita, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Analytical methods to separately evaluate convergence and diversity for multi-objectiveoptimization," Proc. of 14th International Conference of Metaheuristics (MIC 2022), pp. 172-186, Syracuse, Italy, July 11-14, 2022.
  37. Y. Omozaki, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Evolutionary multiobjective multi-tasking for fuzzy genetics-based machine learning in multi-label classification," Proc. of 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8, Padua, Italy, 2022, doi: 10.1109/FUZZ-IEEE55066.2022.9882681.
  38. N. Masuyama, Y. Nojima, H. Ishibuchi, and Z. Liu, "Adaptive resonance theory-based clustering for handling mixed data," Proc. of 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, Padua, Italy, 2022, doi: 10.1109/IJCNN55064.2022.9892060.

    39. 2021
  39. S. A. F. Dilone, N. Masuyama, Y. Nojima, and H. Ishibuchi "Validation data accuracy as an additional objective in multiobjective fuzzy genetics-based machine learning," Proc. of The 22nd International Symposium on Advanced Intelligent Systems (ISIS 2021), Online, December 15-18, 6 pages, 2021.
  40. Y. Yamada, N. Amako, N. Masuyama, Y. Nojima, and H. Ishibuchi "Hierarchical topological clustering with automatic parameter estimation," Proc. of The 22nd International Symposium on Advanced Intelligent Systems (ISIS 2021), Online, December 15-18, 6 pages, 2021.
  41. V. Villin, N. Masuyama, and Y. Nojima, "Effects of different optimization formulations in evolutionary reinforcement learning on diverse behavior generation," Proc. of 2021 IEEE Symposium Series on Computational Intelligence (SSCI 2021), pp. 1-8, Virtual, December 4-7, 2021.
  42. Y. Liu, L. Xu, Y. Han, N. Masuyama, Y. Nojima, H. Ishibuchi, and G. G. Yen, "Multi-modal multi-objective traveling salesman problem and its evolutionary optimizer," Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2021), Virtual, October 17-20, 2021.

    43. 2020
  43. Y. Yamada, N. Masuyama, N. Amako, Y. Nojima, C. K. Loo, and H. Ishibuchi, "Divisive hierarchical clustering based on adaptive resonance theory," Proc. of 2020 International Symposium on Community-centric Systems (CcS 2020), Tokyo, Japan, September 23-26, 2020 (Accepted).
  44. H. Ishibuchi, T. Matsumoto, N. Masuyama, and Y. Nojima, “Many-objective problems are not always difficult for Pareto dominance-based evolutionary algorithms,” Proc. of 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago, Spain, Aug. 29-Sep. 8, 2020 (Accepted).
  45. N. Amako, N. Masuyama, C. K. Loo, Y. Nojima, Y. Liu, and H. Ishibuchi, "Multilayer clustering based on adaptive resonance theory for noisy environments," Proc. of 2020 International Joint Conference on Neural Networks (IJCNN 2020), pp. 1-8, Glasgow, UK, July 19-24, 2020.
  46. R. Hashimoto, T. Urita, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Effects of local mating in inter-task crossover on the performance of decomposition-based evolutionary multiobjective multitask optimization algorithms," Proc. of 2020 IEEE Congress on Evolutionary Computation (CEC 2020), pp. 1-8, Glasgow, UK, July 19-24, 2020.
  47. Y. Liu, H. Ishibuchi, G. G. Yen, Y. Nojima, N. Masuyama, and Y. Han, "On the normalization in evolutionary multi-modal multi-objective optimization," Proc. of 2020 IEEE Congress on Evolutionary Computation (CEC 2020), pp. 1-8, Glasgow, UK, July 19-24, 2020.
  48. Y. Omozaki, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Multiobjective fuzzy genetics-based machine learning for multi-label classification," Proc. of 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020), pp. 1-8, Glasgow, UK, July 19-24, 2020. Best Student Paper Award
  49. H. Ishibuchi, T. Matsumoto, N. Masuyama, and Y. Nojima, "Effects of dominance resistant solutions on the performance of evolutionary multi-objective and many-objective algorithms," Proc. of 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), pp. 507-515, Cancun, Mexico, July 8-12, 2020.
  50. K. van der Blom, T. M. Deist, T. Tušar, M. Marchi, Y. Nojima, A. Oyama, V. Volz, and B. Naujoks, "Towards realistic optimization benchmarks: A questionnaire on the properties of real-world problems," Proc. of 2020 Genetic and Evolutionary Computation Conference Companion (GECCO 2020), pp. 293–294, Cancun, Mexico, July 8-12, 2020. [arXiv]
  51. Y. Xu, L. Chhim, B. Zheng, and Y. Nojima, “Stacked deep learning structure with bidirectional long-short term memory for stock market prediction,” Proc. of First International Conference on Neural Computing for Advanced Applications (NCAA 2020), pp. 447-460, Shenzhen, China, July 3-5, 2020.

    52. 2019
  52. H. Ishibuchi, T. Matsumoto, N. Masuyama, and Y. Nojima, “Optimal distributions of solutions for hypervolume maximization on triangular and inverted triangular Pareto fronts of four-objective Problems,” Proc. of 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019), pp. 1857-1864, Xiamen, China, December 6-9, 2019.
  53. N. Masuyama, C.-K. Loo, H. Ishibuchi, N. Amako, Y. Nojima, and Y. Liu, “Fast topological adaptive resonance theory based on correntropy induced metric,” Proc. of 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019), pp. 2215-2221, Xiamen, China, December 6-9, 2019.
  54. R. Hashimoto, N. Masuyama, Y. Nojima, and H. Ishibuchi, “Effect of solution information sharing between tasks on the search ability of evolutionary multiobjective multitasking algorithms,” Proc. of 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019), pp. 2681-2688, Xiamen, China, December 6-9, 2019.
  55. T. Fukase, N. Masuyama, Y. Nojima, Y. Liu, and H. Ishibuchi, “Dots-type constrained multiobjective distance minimization problems,” Proc. of 20th International Symposium on Advanced Intelligent Systems and 2019 International Conference on Biometrics and Kansei Engineering (ISIS2019 & ICBAKE 2019), pp. 51-56, Jeju Island, Korea, December 4-7, 2019.
  56. Y. Omozaki, N. Masuyama, Y. Nojima, and H. Ishibuchi, “Development of a GUI tool for FML-based fuzzy system modeling,” Proc. of 20th International Symposium on Advanced Intelligent Systems and 2019 International Conference on Biometrics and Kansei Engineering (ISIS2019 & ICBAKE 2019), pp. 116-121, Jeju Island, Korea, December 4-7, 2019.
  57. Y. Nojima, T. Fukase, Y. Liu, N. Masuyama, and H. Ishibuchi, "Constrained multiobjective distance minimization problems," Proc. of 2019 Genetic and Evolutionary Computation Conference, pp. 586-594, Prague, Czech Republic, July 13-17, 2019.
  58. T. Matsumoto, N. Masuyama, Y. Nojima, and H. Ishibuchi, "A multiobjective test suite with hexagon Pareto fronts and various feasible regions," Proc. of 2019 IEEE Congress on Evolutionary Computation, pp. 2059-2066, Wellington, New Zealand, June 10-13, 2019.
  59. H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, "Two-layered weight vector specification in decomposition-based multi-objective algorithms for many-objective optimization problems," Proc. of 2019 IEEE Congress on Evolutionary Computation, pp. 2435-2442, Wellington, New Zealand, June 10-13, 2019.
  60. Y. Liu, H. Ishibuchi, Y. Nojima, N. Masuyama, and Y. Han, "Searching for local Pareto optimal solutions: A case study on polygon-based problems," Proc. of 2019 IEEE Congress on Evolutionary Computation, pp. 873-880, Wellington, New Zealand, June 10-13, 2019.
  61. C.-S. Lee, M.-H. Wang, L.-C. Chen, Y. Nojima, T.-X. Huang, J. Woo, N. Kubota, E. Sato-Shimokawara, and T. Yamaguchi, "A GFML-based robot agent for human and machine cooperative learning on game of Go," Proc. of 2019 IEEE Congress on Evolutionary Computation, pp. 770-776, Wellington, New Zealand, June 10-13, 2019. [arXiv]
  62. H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, "Comparison of hypervolume, IGD and IGD+ from the viewpoint of optimal distributions of solutions," Proc. of 10th International Conference on Evolutionary Multi-Criterion Optimization, pp. 332-345, East Lansing, USA, March 10-13, 2019. Springer Best Paper Award - 1st Prize

    63. 2018
  63. Y. Irie, N. Masuyama, Y. Nojima, and H. Ishibuchi, "A preliminary study of Michigan-style fuzzy genetics-based machine learning for class incremental problems," Proc. of 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, pp. 713-717, Toyama, Japan, Dec. 5-8, 2018.
  64. Y. Nojima, Y. Tanigaki, N. Masuyama, and H. Ishibuchi, "Multiobjective evolutionary data mining for performance improvement of evolutionary multiobjective optimization," Proc. of 2018 IEEE International Conference on Systems, Man, and Cybernetics, pp. 741-746, Miyazaki, Japan, Oct. 7-10, 2018.[PDF]
  65. T. Matsumoto, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Performance comparison of multiobjective evolutionary algorithms on problems with partially different properties from popular scalable test suites," Proc. of 2018 IEEE International Conference on Systems, Man, and Cybernetics, pp. 765-770, Miyazaki, Japan, Oct. 7-10, 2018.
  66. Y. Liu, H. Ishibuchi, Y. Nojima, N. Masuyama, and K. Shang, "A double-niched evolutionary algorithm and its behaviors on polygon-based problems," Proc. of 15th International Conference on Parallel Problem Solving from Nature, pp. 262-273, Coimbra, Portugal, Sep. 8-12, 2018.
  67. Y. Liu, H. Ishibuchi, Y. Nojima, N. Masuyama, and K. Shang, "Improving 1by1EA to handle various shapes of Pareto fronts," Proc. of 15th International Conference on Parallel Problem Solving from Nature, pp. 311-322, Coimbra, Portugal, Sep. 8-12, 2018.
  68. H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, "Use of two reference points in hypervolume-based evolutionary multiobjective optimization algorithms," Proc. of 15th International Conference on Parallel Problem Solving from Nature, pp. 384-396, Coimbra, Portugal, Sep. 8-12, 2018.
  69. Y. Nojima, S. Sakai, N. Masuyama, and H. Ishibuchi, "Multiobjective evolutionary classifier design using class scores by a deep convolutional neural network," PPSN 2018 Workshop on Evolutionary Machine Learning, Coimbra, Portugal, Sep. 8-12, 2018.
  70. R. Hashimoto, H. Ishibuchi, N. Masuyama, and Y. Nojima, "Analysis of evolutionary multi-tasking as an island model," Companion of 2018 Genetic and Evolutionary Computation Conference, pp. 1894-1897, Kyoto, Japan, July 2018.
  71. H. Ishibuchi, T. Fukase, N. Masuyama, and Y. Nojima, "Dual-grid model of MOEA/D for evolutionary constrained multiobjective optimization," Proc. of 2018 Genetic and Evolutionary Computation Conference, pp. 665-672, Kyoto, Japan, July 2018.
  72. H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, "Dynamic specification of a reference point for hypervolume calculation in SMS-EMOA," Proc. of 2018 IEEE Congress on Evolutionary Computation, pp. 701-708, Rio de Janeiro, Brazil, July 2018.
  73. N. Masuyama, C. K. Loo, H. Ishibuchi, Y. Nojima, and Y. Liu, "Topological kernel Bayesian ARTMAP," Proc. of World Automation Congress, pp. 316-321, Washington, USA, June 3-6, 2018.

    74. 2017
  74. K. Doi, R. Imada, Y. Nojima, and H. Ishibuchi, “Use of inverted triangular weight vectors in decomposition-based many-objective algorithms,” Proc. of 11th International Conference on Simulated Evolution and Learning, pp. 321-333, Shenzhen, China, Nov. 10-13, 2017.
  75. H. Gao, Y. Nojima, and H. Ishibuchi, “Multi-objective GAssist with NSGA-II,” Proc. of 18th International Symposium on Advanced Intelligent Systems, pp. 696-703, Deagu, Republic of Korea, October 11-14, 2017.
  76. H. Ishibuchi, R. Imada, K. Doi, and Y. Nojima, "Use of inverted triangular weight vectors in decomposition-based multiobjective algorithms," Proc. of 2017 IEEE International Conference on Systems, Man, and Cybernetics, pp. 373-378, Banff, Canada, October 5-8, 2017.
  77. H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Reference point specification in hypervolume calculation for fair comparison and efficient search,” Proc. of 2017 Genetic and Evolutionary Computation Conference, pp. 585-592, Berlin, Germany, July 15-19, 2017. Best Paper Award [PDF]
  78. Y. Nojima, Y. Tanigaki, and H. Ishibuchi, “Multiobjective data mining from solutions by evolutionary multiobjective optimization,” Proc. of 2017 Genetic and Evolutionary Computation Conference, pp. 617-624, Berlin, Germany, July 15-19, 2017. [PDF]
  79. Y. Nojima, K. Arahari, S. Takemura, and H. Ishibuchi, “Multiobjective fuzzy genetics-based machine learning based on MOEA/D with its modifications,” Proc. of 2017 IEEE International Conference on Fuzzy Systems, 6 pages, Naples, Italy, July 9-12, 2017. [PDF]
  80. Y. Nojima, S. Takemura, K. Watanabe, and H. Ishibuchi, "Michigan-style fuzzy GBML with (1+1)-ES generation update and multi-pattern rule generation," Proc. of Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, 6 pages, Otsu, Japan, June 27-30, 2017.
  81. Y. Tanigaki, Y. Nojima, and H. Ishibuchi, "Performance comparison of EMO algorithms on test problems with different search space shape," Proc. of Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, 6 pages, Otsu, Japan, June 27-30, 2017.
  82. C.-S. Lee, M.-H. Wang, C.-H. Kao, S.-C. Yang, Y. Nojima, R. Saga, N. Shuo, and N. Kubota, "FML-based prediction agent and its application to game of Go," Proc. of Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, 6 pages, Otsu, Japan, June 27-30, 2017. [arXiv]
  83. H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Hypervolume subset selection for triangular and inverted triangular Pareto fronts of three-objective problems,” Proc. of 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA 2017), pp. 95-110, Copenhagen, Denmark, January 12-15, 2017. [PDF]

    84. 2016
  84. H. Zille, H. Ishibuchi, S. Mostaghim, and Y. Nojima, "Mutation operators based on variable grouping for multi-objective large-scale optimization," Proc. of 2016 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, 8 pages, Athens, Greece, December 6-9, 2016.
  85. H. Ishibuchi, S. Takemura, and Y. Nojima, “Fitting and overfitting of multi-objective fuzzy genetics-based machine learning to training data,” Proc. of 7th International Symposium on Computational Intelligence and Industrial Applications, 6 pages, Beijing, China, November 3-6, 2016.
  86. H. Ishibuchi, K. Doi, and Y. Nojima, “Difficulties of MOEA/D with Tchebycheff function for many-objective DTLZ 1-4 problems,” Proc. of 7th International Symposium on Computational Intelligence and Industrial Applications, 6 pages, Beijing, China, November 3-6, 2016.
  87. H. Ishibuchi, K. Doi, and Y. Nojima, “Reference point specification in MOEA/D for multi-objective and many-objective problems,” Proc. of 2016 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4015-4020, Budapest, Hungary, October 9-12, 2016.
  88. H. Ishibuchi, K. Doi, and Y. Nojima, “Use of piecewise linear and nonlinear scalarizing functions in MOEA/D,” Proc. of 14th International Conference on Parallel Problem Solving from Nature, pp. 503-523, Edinburgh, Scotland, UK, September 17-21, 2016.
  89. T. Funakoshi, Y. Nojima, and H. Ishibuchi, “Effects of different implementations of a real random number generator on the search behavior of multiobjective evolutionary algorithms,” Proc. of Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, pp. 172-177, Sapporo, Hokkaido, August 26-28, 2016.
  90. H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, “Performance comparison of NSGA-II and NSGA-III on various many-objective test problems,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 3045-3052, Vancouver, Canada, July 24-29, 2016.
  91. H. Masuda, Y. Nojima, and H. Ishibuchi, “Common properties of scalable multiobjective problems and a new framework of test problems,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 3011-3018, Vancouver, Canada, July 24-29, 2016.
  92. Y. Tanigaki, Y. Nojima, and H. Ishibuchi, “Meta-optimization based multi-objective test problem generation using WFG toolkit,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 2768-2775, Vancouver, Canada, July 24-29, 2016.
  93. Y. Nojima and H. Ishibuchi, “Effects of parallel distributed implementation on the search performance of Pittsburgh-style genetics-based machine learning algorithms,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 2193-2200, Vancouver, Canada, July 24-29, 2016. [PDF]
  94. H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, “How to compare many-objective algorithms under different settings of population and archive sizes,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 1149-1156, Vancouver, Canada, July 24-29, 2016.
  95. H. Ishibuchi, K. Doi, and Y. Nojima, “Characteristics of many-objective test problems and penalty parameter specification in MOEA/D,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 1115-1122, Vancouver, Canada, July 24-29, 2016.
  96. H. Ishibuchi, H. Masuda, and Y. Nojima, “Sensitivity of performance evaluation results by inverted generational distance to reference points,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 1107-1114, Vancouver, Canada, July 24-29, 2016.
  97. T. Sudo, K. Goto, Y. Nojima, and H. Ishibuchi, “Further analysis on strange evolution behavior of 7-bit binary string strategies in iterated prisoner’s dilemma game,” Proc. of 2016 IEEE Congress on Evolutionary Computation, pp. 335-342, Vancouver, Canada, July 24-29, 2016.
  98. Y. Nojima and H. Ishibuchi, “Multiobjective fuzzy genetics-based machine learning with a reject option,” Proc. of 2016 IEEE International Conference on Fuzzy Systems, pp. 1405-1412, Vancouver, Canada, July 24-29, 2016. [PDF]
  99. H. Zille, H. Ishibuchi, S. Mostaghim, and Y. Nojima, “Weighted optimization framework for large-scale multi-objective optimization,” Companion of 2016 Genetic and Evolutionary Computation Conference, pp. 83-84, Denver, USA, July 20-24, 2016.

    100. 2015
  100. H. Ishibuchi, K. Doi, H. Masuda, and Y. Nojima, "Relation between weight vectors and solutions in MOEA/D," Proc. of 2015 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, pp. 861-868, Cape Town, December 8-10, 2015.
  101. Y. Nojima, K. Watanabe, and H. Ishibuchi, “Variants of heuristic rule generation from multiple patterns in Michigan-style fuzzy genetics-based machine learning,” Proc. of 2015 Conference on Technologies and Applications of Artificial Intelligence, pp. 427-432, Tainan, Taiwan, Nov. 20-22, 2015. [PDF] Merit Paper Award
  102. Y. Nojima, K. Watanabe, and H. Ishibuchi, “Simple modifications on heuristic rule generation and rule evaluation in Michigan-style fuzzy genetics-based machine learning,” Proc. of 2015 IEEE International Conference on Fuzzy Systems, 8 pages, Istanbul, Turkey, August 2-5, 2015. [PDF]
  103. H. Ishibuchi and Y. Nojima, “Handling a training dataset as a black-box model for privacy preserving in fuzzy GBML algorithms,” Proc. of 2015 IEEE International Conference on Fuzzy Systems, 8 pages, Istanbul, Turkey, August 2-5, 2015.
  104. H. Ishibuchi, H. Masuda, and Y. Nojima, "A study on performance evaluation ability of a modified inverted generational distance indicator," Proc. of Genetic and Evolutionary Computation Conference, pp. 695-702, Madrid, Spain, July 11-15, 2015.
  105. Y. Takahashi, Y. Nojima, and H. Ishibuchi, "Rotation effects of objective functions in parallel distributed multiobjective fuzzy genetics-based machine learning," Proc. of 10th Asian Control Conference, 6 pages, Kota Kinabalu, Malaysia, May 31-June 3, 2015.
  106. Y. Tanigaki, H. Masuda, Y. Setoguchi, Y. Nojima, and H. Ishibuchi, "Algorithm structure optimization by choosing operators in multiobjective genetic local search," Proc. of 2015 IEEE Congress on Evolutionary Computation, pp. 854-861, Sendai, Japan, May 25-28, 2015.
  107. T. Sudo, K. Goto, Y. Nojima, and H. 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," Proc. of 2015 IEEE Congress on Evolutionary Computation, pp. 1505-1512, Sendai, Japan, May 25-28, 2015.
  108. H. Ishibuchi, H. Masuda, and Y. Nojima, "Comparing solution sets of different size in evolutionary many-objective optimization," Proc. of 2015 IEEE Congress on Evolutionary Computation, pp. 2859-2866, Sendai, Japan, May 25-28, 2015.
  109. Y. Nojima, K. Watanabe, and H. Ishibuchi, "Effects of heuristic rule generation from multiple patterns in multiobjective fuzzy genetics-based machine learning," Proc. of 2015 IEEE Congress on Evolutionary Computation, pp. 2996-3003, Sendai, Japan, May 25-28, 2015. [PDF]
  110. T. Sudo, K. Goto, Y. Nojima, and H. Ishibuchi, "Strange evolution behavior of 7-bit binary string strategies in iterated prisoner's dilemma game," Proc. of 2015 IEEE Congress on Evolutionary Computation, pp. 3346-3353, Sendai, Japan, May 25-28, 2015.
  111. H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, "Modified distance calculation in generational distance and inverted generational distance," Proc. of 8th International Conference on Evolutionary Multi-Criterion Optimization, Part I, pp. 110-125, Guimarães, Portugal, March 29-April 1, 2015.
  112. Y. Nojima, Y. Takahashi, and H. Ishibuchi, "Application of parallel distributed implementation to multiobjective fuzzy genetics-based machine learning," Proc. of 7th Asian Conference on Intelligent Information and Database Systems, Part I, pp. 462-471, Bali, Indonesia, March 23-25, 2015. Best Regular Paper Award

    113. 2014
  113. H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, "Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems," Proc. of 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, pp. 170-177, Orlando, Florida, USA, December 9-12, 2014.
  114. H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, "Review of coevolutionary developments of evolutionary multi-objective and many-objective algorithms and test problems," Proc. of 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, pp. 178-185, Orlando, Florida, USA, December 9-12, 2014.
  115. Y. Tanigaki, K. Narukawa, Y. Nojima, and H. Ishibuchi, "Preference-based NSGA-II for many-objective knapsack problems," Proc. of 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems, pp. 637-642, Kitakyushu, Japan, December 3-6, 2014.
  116. Y. Nojima, Y. Takahashi, and H. Ishibuchi, "Genetic lateral tuning of membership functions as post-processing for hybrid fuzzy genetics-based machine learning," Proc. of 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems, pp. 667-672, Kitakyushu, Japan, December 3-6, 2014.
  117. H. Ishibuchi, T. Sudo, K. Ueba, and Y. Nojima, "Offline design of interactive evolutionary algorithms with different genetic operators at each generation," Proc. of 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, vol. 2, pp. 635-646, Singapore, November 10-12, 2014.
  118. H. Ishibuchi, H. Masuda, and Y. Nojima, "Selecting a small number of non-dominated solutions to be presented to the decision maker," Proc. of 2014 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3850-3855, San Diego, CA, USA, October 5-8, 2014.
  119. H. Ishibuchi, Y. Tanigaki, H. Masuda, and Y. Nojima, "Distance-based analysis of crossover operators for many-objective knapsack problems," Proc. of 13th International Conference on Parallel Problem Solving from Nature, pp. 600-610, Ljubljana, Slovenia, September 13-17, 2014.
  120. H. Ishibuchi, H. Masuda, and Y. Nojima, "Meta-level multi-objective formulations of set optimization for multi-objective optimization problems: Multi-reference point approach to hypervolume maximization," Companion of 2014 Genetic and Evolutionary Computation Conference, pp. 89-90, Vancouver, Canada, July 12-16, 2014.
  121. T. Sudo, Y. Nojima, and H. Ishibuchi, "Effects of ensemble action selection on the evolution of iterated prisoner's dilemma game strategies," Proc. of 2014 IEEE Congress on Evolutionary Computation, pp. 1195-1201, Beijing, China, July 6-11, 2014.
  122. H. Masuda, Y. Nojima, and H. Ishibuchi, "Visual examination of the behavior of EMO algorithms for many-objective optimization with many decision variables," Proc. of 2014 IEEE Congress on Evolutionary Computation, pp. 2633-2640, Beijing, China, July 6-11, 2014.
  123. Y. Takahashi, Y. Nojima, and H. Ishibuchi, "Hybrid fuzzy genetics-based machine learning with entropy-based inhomogeneous interval discretization," Proc. of 2014 IEEE International Conference on Fuzzy Systems, pp. 1512-1517, Beijing, China, July 6-11, 2014.
  124. H. Ishibuchi, T. Sudo, and Y. Nojima, "Archive management in interactive evolutionary computation with minimum requirement for human user's fitness evaluation ability," Proc. of 13th International Conference on Artificial Intelligence and Soft Computing, pp. 360-371, Zakopane, Poland, June 1-5, 2014.
  125. T. Sudo, K. Ueba, Y. Nojima, H. Ishibuchi, "Interactive (1+1) evolutionary strategy with one-fifth success rule," Proc. of 2nd Asia-Pacific Conference on Computer Aided System Engineering, pp. 294-300, Bali, Indonesia, February 10-12, 2014.
  126. H. Ishibuchi, M. Yamane, and Y. Nojima, "Evolutionary multiobjective design of fuzzy rule-based classifiers with explanation ability," Conference on Business Analytics in Finance and Industry (BAFI 2014), One-Page Abstract, Santiago, Chili, January 6-9, 2014.

    127. 2013
  127. Y. Nojima, Y. Takahashi, M. Yamane, and H. Ishibuchi, "Environmental selection schemes for rule removal in Michigan-style fuzzy genetics-based machine learning," Proc. of 14th International Symposium on Advanced Intelligent Systems (USB 10 pages), Daejeon, Korea, November 13-16, 2013.
  128. Y. Nojima, P. Ivarsson, and H. Ishibuchi, "Application of parallel distributed implementation to GAssist and its sensitivity analysis on the number of sub-populations and training data subsets," Proc. of 14th International Symposium on Advanced Intelligent Systems (USB 10 pages), Daejeon, Korea, November 13-16, 2013. Best Session Paper Award
  129. H. Ishibuchi, T. Sudo, K. Hoshino, and Y. Nojima, "Effects of the number of opponents on the evolution of cooperation in the iterated prisoner's dilemma," Proc. of 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2001-2006, Manchester, UK, October 13-16, 2013.
  130. H. Ishibuchi, T. Sudo, K. Hoshino, and Y. Nojima, "Evolutionary learning of the complexity of game strategies for iterated prisoner's dilemma," International Symposium on Prediction and Decision Making 2013, One-page abstract, p. 53, Kyoto, Japan, October 13-14, 2013.
  131. H. Ishibuchi, M. Yamane, and Y. Nojima, "Learning from multiple data sets with different missing attributes and privacy policies: Parallel distributed fuzzy genetics-based machine learning approach," Proc. of IEEE Big Data 2013 Workshop on Scalable Machine Learning: Theory and Applications, pp. 63-70, Santa Clara, CA, USA, October 6-9, 2013.
  132. H. Ishibuchi, T. Sudo, K. Hoshino, and Y. Nojima, "Evolution of cooperative strategies for iterated prisoner's dilemma on networks," Proc. of Fifth International Conference on Computational Aspects of Social Networks (CASoN), pp. 32-37, Fargo, USA, August 12-14, 2013.
  133. H. Ishibuchi, M. Yamane, and Y. Nojima, "Rule weight update in parallel distributed fuzzy genetics-based machine learning with data rotation," Proc. of 2013 IEEE International Conference on Fuzzy Systems, in CD-ROM (8 pages), Hyderabad, India, July 10-15, 2013.
  134. H. Ishibuchi and Y. Nojima, "Difficulties in choosing a single final classifier from non-dominated solutions in multiobjective fuzzy genetics-based machine learning," Proc. of 2013 Joint IFSA World Congress NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 1203-1208, Edmonton, Canada, June 24-28, 2013.
  135. H. Ishibuchi, Y. Tanigaki, N. Akedo, and Y. Nojima, "How to strike a balance between local search and global search in multiobjective memetic algorithms for multiobjective 0/1 knapsack problems," Proc. of 2013 IEEE Congress on Evolutionary Computation, pp. 1643-1650, Cancun, Mexico, June 20-23, 2013.
  136. H. Ishibuchi, M. Yamane, N. Akedo, and Y. Nojima, "Many-objective and many-variable test problems for visual examination of multiobjective search," Proc. of 2013 IEEE Congress on Evolutionary Computation, pp. 1491-1498, Cancun, Mexico, June 20-23, 2013.
  137. Y. Nojima and H. Ishibuchi, "Multiobjective genetic fuzzy rule selection with fuzzy relational rules," Proc. of 2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 60-67, Singapore, April 16-19, 2013. [PDF]
  138. M. Fazzolari, R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, "Improving a fuzzy association rule-based classification model by granularity learning based on heuristic measures over multiple granularities," Proc. of 2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 44-51, Singapore, April 16-19, 2013.
  139. H. Ishibuchi, M. Yamane, and Y. Nojima, "Effects of duplicated objectives in many-objective optimization problems on the search behavior of hypervolume-based evolutionary algorithms," Proc. of 2013 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, pp. 25-32, Singapore, April 16-19, 2013.
  140. H. Ishibuchi, N. Akedo, and Y. Nojima, "Relation between neighborhood size and MOEA/D performance on many-objective problems," Proc. of 7th International Conference on Evolutionary Multi-Criterion Optimization, pp. 459-474, Sheffield, UK, March 19-22, 2013.
  141. H. Ishibuchi, M. Yamane, and Y. Nojima, "Difficulty in evolutionary multiobjective optimization of discrete objective functions with different granularities," Proc. of 7th International Conference on Evolutionary Multi-Criterion Optimization, pp. 230-245, Sheffield, UK, March 19-22, 2013.
  142. H. Ishibuchi, N. Akedo, and Y. Nojima, "A study on the specification of a scalarizing function in MOEA/D for many-objective knapsack problems," Proc. of 7th International Conference on Learning and Intelligent Optimization, pp. 231-246, Catania, Italy, January 7-11, 2013.
  143. H. Ishibuchi, K. Hoshino, and Y. Nojima, "Neighborhood specification for game strategy evolution in a spatial iterated prisoner's dilemma game," Proc. of 7th International Conference on Learning and Intelligent Optimization, pp. 215-230, Catania, Italy, January 7-11, 2013.

    144. 2012
  144. H. Ishibuchi, M. Yamane, and Y. Nojima, "Ensemble fuzzy rule-based classifier design by parallel distributed fuzzy GBML algorithms," Proc. of 9th International Conference on Simulated Evolution and Learning - SEAL 2012, pp. 93-103, Hanoi, Vietnam, December 16-19, 2012.
  145. H. Ishibuchi, K. Hoshino, and Y. Nojima, "Problem formulation of interactive evolutionary computation with minimum requirement for human user's fitness evaluation ability," Proc. of 16th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 52-57, Kyoto, Japan, December 12-14, 2012.
  146. H. Ishibuchi, M. Yamane, N. Akedo, and Y. Nojima, "Two-Objective Solution Set Optimization to Maximize Hypervolume and Decision Space Diversity in Multiobjective Optimization," Proc. of Joint 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligent Systems, pp. 1871-1876, Kobe, Japan, November 20-24, 2012.
  147. M. Yamane, A. Ueda, N. Tadokoro, Y. Nojima, and H. Ishibuchi, "Comparison of Different Fitness Functions in Genetic Fuzzy Rule Selection," Proc. of Joint 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligent Systems, pp. 1046-1051, Kobe, Japan, November 20-24, 2012.
  148. H. Ishibuchi, N. Akedo, and Y. Nojima, "Recombination of similar parents in SMS-EMOA on many-objective 0/1 knapsack problems," Proc. of 12th International Conference on Parallel Problem Solving from Nature, Part II, pp. 132-142, Taormina, Italy, September 1-5, 2012.
  149. H. Ishibuchi, M. Yamane, and Y. Nojima, "Effects of Discrete Objective Functions with Different Granularities on the Search Behavior of EMO Algorithms," Proc. of 2012 Genetic and Evolutionary Computation Conference - GECCO 2012, pp. 481-488, Philadelphia, USA, July 7-11, 2012.
  150. H. Ishibuchi, N. Akedo, and Y. Nojima, "EMO algorithms on correlated many-objective problems with different correlation strength," Proc. of 2012 World Automation Congress, Puerto Vallarta, Mexico, June 24-27, 2012 (6 pages).
  151. Y. Nojima, S. Mihara, and H. Ishibuchi, "Application of parallel distributed genetics-based machine learning to imbalanced data sets," Proc. of 2012 IEEE International Conference on Fuzzy Systems, pp. 928-933, Brisbane, Australia, June, 10-15, 2012. [PDF]
  152. H. Ishibuchi, K. Hoshino, and Y. Nojima, "Evolution of strategies in a spatial IPD Game with a number of different representation schemes," Proc. of 2012 IEEE Congress on Evolutionary Computation, pp. 808-815, Brisbane, Australia, June, 10-15, 2012.
  153. H. Ishibuchi, K. Hoshino, and Y. Nojima, "Strategy evolution in a spatial IPD game where each agent is not allowed to play against itself," Proc. of 2012 IEEE Congress on Evolutionary Computation, pp. 688-695, Brisbane, Australia, June, 10-15, 2012.

    154. 2011
  154. H. Ishibuchi, S. Mihara, and Y. Nojima, "Training data subdivision and periodical rotation in hybrid fuzzy genetics-based machine learning," Proc. of 10th International Conference on Machine Learning and Applications, pp. 229-234, Honolulu, Hawaii, USA, December 18-21, 2011.
  155. Y. Nojima and H. Ishibuchi, "Mobile robot controller design by evolutionary multiobjective optimization in multiagent environments," Lecture Notes in Artificial Intelligence 7102: Intelligent Robotics and Applications - ICIRA 2011, Part II, pp. 515-524, Springer, Heidelberg, December 2011.
  156. S. Fukuda, B. De Baets, and Y. Nojima, "Comparing predictive accuracy of a genetic Takagi-Sugeno fuzzy model and random forests for fish habitat modelling," Proc. of International Workshop on Advanced Computational Intelligence and Intelligent Informatics, (CD-ROM, 6 pages), Suzhou, China, November 19-23, 2011.
  157. Y. Nojima, S. Mihara, and H. Ishibuchi, "Parallel distributed genetic rule selection of association rules," Abstract Booklet of International Workshop on Simulation and Modeling related to Computational Science and Robotics Technology, pp. 34-35, Kobe, Japan, November 1-3, 2011.
  158. S. Mihara. Y. Nojima, and H. Ishibuchi, "Relation between migration interval and data rotation interval in parallel distributed fuzzy GBML," Proc. of 12th International Symposium on Advanced Intelligent Systems, pp. 346-349, Suwon, Korea, September 29 - October 1, 2011.
  159. H. Ishibuchi, K. Takahashi, K. Hoshino, J. Maeda, and Y. Nojima, "Effects of configuration of agents with different strategy representations on the evolution of cooperative behavior in a spatial IPD game," Proc. of 2011 IEEE Conference on Computational Intelligence and Games- CIG 2011, pp. 313-320, Seoul, Korea, August 31 - September 3, 2011.
  160. H. Ishibuchi, N. Akedo, and Y. Nojima, "A many-objective test problem for visually examining diversity maintenance behavior in a decision space," Proc. of 2011 Genetic and Evolutionary Computation Conference - GECCO 2011, pp. 649-656, Dublin, Ireland, July 12-16, 2011.
  161. H. Ishibuchi and Y. Nojima, "Toward quantitative definition of explanation ability of fuzzy rule-based classifiers," Proc. of 2011 IEEE International Conference on Fuzzy Systems, pp. 549-556, Taipei, Taiwan, June 27-30, 2011. Best Paper Award
  162. Y. Nojima, S. Nishikawa, and H. Ishibuchi, "A meta-fuzzy classifier for specifying appropriate fuzzy partitions by genetic fuzzy rule selection with data complexity measures," Proc. of 2011 IEEE International Conference on Fuzzy Systems, pp. 264-271, Taipei, Taiwan, June 27-30, 2011. [PDF]
  163. H. Ishibuchi, N. Akedo, H. Ohyanagi, and Y. Nojima, "Behavior of EMO algorithms on many-objective optimization problems with correlated objectives," Proc. of 2011 IEEE Congress on Evolutionary Computation, (CD-ROM, 8 pages), New Orleans, USA, June 5-8, 2011
  164. H. Ishibuchi, Y. Nakashima, and Y. Nojima, "Double cross-validation for performance evaluation of multi-objective genetic fuzzy systems," Proc. of 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 31-38, Paris, France, April 11-15, 2011.
  165. H. Ishibuchi, N. Akedo, H. Ohyanagi, Y. Hitotsuyanagi, and Y. Nojima, "Many-objective test problems with multiple Pareto optimal regions in a decision space," Proc. of 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, pp. 113-120, Paris, France, April 11-15, 2011.
  166. H. Ishibuchi, Y. Hitotsuyanagi, H. Ohyanagi, and Y. Nojima, "Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D", Proc. of 6th International Conference on Evolutionary Multi-Criterion Optimization, pp. 166-181, Ouro Preto, Brazil, April 5-8, 2011.

    167. 2010
  167. S. Nishikawa, Y. Nojima, and H. Ishibuchi, "Appropriate granularity specification for fuzzy classifier design by data complexity measures," Proc. of the World Congress on Nature and Biologically Inspired Computing, pp. 698-703, Kitakyushu, Japan, December 15-17, 2010.
  168. H. Ishibuchi, Y. Hitotsuyanagi, Y. Nakashima, and Y. Nojima, "Multiobjectivization from two objectives to four objectives in evolutionary multi-objective optimization algorithms," Proc. of the World Congress on Nature and Biologically Inspired Computing, pp. 509-514, Kitakyushu, Japan, December 15-17, 2010.
  169. H. Ishibuchi, Y. Sakane, and Y. Nojima, "Use of multiple grids with different scalarizing functions in MOEA/D," Proc. of Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, pp. 898-903, Okayama, Japan, December 8-12, 2010. Best Paper Award
  170. Y. Nojima, S. Mihara, and H. Ishibuchi, "Parallel distributed implementation of genetics-based machine learning for fuzzy classifier design," Lecture Notes in Computer Science 6457: Simulated Evolution and Learning (8th International Conference on Simulated Evolution and Learning), pp. 309-318, Springer, Berlin, December 2010. [PDF]
  171. Y. Nojima, S. Mihara, and H. Ishibuchi, "Rotation effect of training data subsets in parallel distributed fuzzy genetics-based machine learning," Proc. of 14th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 96-105, Miyajima, Japan, November 19-20, 2010.[PDF]
  172. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Use of non-geometric binary crossover as mutation," Proc. of World Automation Congress - WAC 2010 (CD-ROM, 6 pages), Kobe, Japan, September 19-22, 2010. Best Paper Award
  173. H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, "Many-objective test problems to visually examine the behavior of multiobjective evolution in a decision space," Proc. of 11th International Conference on Parallel Problem Solving from Nature, Part II, pp. 91-100, Krakow, Poland, September 11-15, 2010.
  174. H. Ishibuchi, Y. Hitotsuyanagi, Y. Wakamatsu, and Y. Nojima, "How to choose solutions for local search in multiobjective combinatorial memetic algorithms," Proc. of 11th International Conference on Parallel Problem Solving from Nature, Part I, pp. 516-525, Krakow, Poland, September 11-15, 2010.
  175. Y. Nojima, S. Mihara, and H. Ishibuchi, "Ensemble classifier design by parallel distributed implementation of genetic fuzzy rule selection for large data sets," Proc. of 2010 IEEE Congress on Evolutionary Computation, pp. 2113-2120, Barcelona, Spain, July 18-23, 2010. [PDF]
  176. Y. Nojima, Y. Kaisho, and H. Ishibuchi, "Accuracy implementation of genetic fuzzy rule selection with candidate rule addition and membership tuning," Proc. of 2010 IEEE International Conference on Fuzzy Systems, pp. 527-534, Barcelona, Spain, July 18-23, 2010. [PDF]
  177. H. Ishibuchi, Y. Nakashima, and Y. Nojima, "Effects of fine fuzzy partitions on the generalization ability of evolutionary multi-objective fuzzy rule-based classifiers," Proc. of 2010 IEEE International Conference on Fuzzy Systems, pp. 1238-1245, Barcelona, Spain, July 18-23, 2010.
  178. H. Ishibuchi, Y. Sakane, N. Tsukamoto and Y. Nojima, "Simultaneous use of different scalarizing functions in MOEA/D," Proc. of Genetic and Evolutionary Computation Conference - GECCO 2010, pp. 519-526, Portland, USA, July 7-11, 2010.
  179. H. Ishibuchi, N. Tsukamoto, Y. Sakane and Y. Nojima, "Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions," Proc. of Genetic and Evolutionary Computation Conference - GECCO 2010, pp. 527-534, Portland, USA, July 7-11, 2010.
  180. Y. Nojima, H. Ishibuchi, and S. Mihara, "Use of very small training data subsets in parallel distributed genetic fuzzy rule selection," Proc. of 4th International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 27-32, Mieres, Spain, March 17-19, 2010. [PDF]
  181. H. Ishibuchi, Y. Nakashima, and Y. Nojima, "Simple changes in problem formulations make a difference in multiobjective genetic fuzzy systems," Proc. of 4th International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 3-8, Mieres, Spain, March 17-19, 2010.

    182. 2009
  182. Y. Nojima, Y. Nakashima, and H. Ishibuchi, "Effects of the user of multiple fuzzy partitions on the search ability of multiobjective fuzzy genetics-based machine learning," Proc. of International Conference on Soft Computing and Pattern Recognition, pp. 341-346, Malacca, Malaysia, December 4-7, 2009.
  183. Y. Tsujimoto, Y. Hitotsuyanagi, Y. Nojima, and H. Ishibuchi, "Effects of including single-objective optimal solutions in an initial population on evolutionary multiobjective optimization," Proc. of International Conference on Soft Computing and Pattern Recognition, pp. 352-357, Malacca, Malaysia, December 4-7, 2009.
  184. Y. Nojima and H Ishibuchi, "Effects of data reduction on the generalization ability of parallel distributed genetic fuzzy rule selection," Proc. of 9th International Conference on Intelligent Systems Design and Applications, pp. 96-101, Pisa, Italy, November 30-December 2, 2009. [PDF]
  185. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations," Proc. of 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1820-1825, San Antonio, USA, October 10-13, 2009.
  186. H. Ishibuchi, H. Ohyanagi, and Y. Nojima, "Evolution of cooperative behavior in a spatial Iterated Prisoner's Dilemma game with different representation schemes of game strategies," Proc. of 2009 IEEE International Conference on Fuzzy Systems, pp. 1568-1573, Jeju Island, Korea, August 20-24, 2009.
  187. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Selecting a small number of representative non-dominated solutions by a hypervolume-based solution selection approach," Proc. of 2009 IEEE International Conference on Fuzzy Systems, pp. 1609-1614, Jeju Island, Korea, August 20-24, 2009. Best Paper Award
  188. R. Alcala, Y. Nojima, F. Herrera, and H. Ishibuchi, "Generating single granularity-based fuzzy classification rules for multiobjective genetic fuzzy rule selection," Proc. of 2009 IEEE International Conference on Fuzzy Systems, pp. 1718-1723, Jeju Island, Korea, August 20-24, 2009.
  189. H. Ishibuchi, Y. Nakashima, and Y. Nojima, "Search ability of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning," Proc. of 2009 IEEE International Conference on Fuzzy Systems, pp. 1724-1729, Jeju Island, Korea, August 20-24, 2009.
  190. H. Ishibuchi, Y. Kaisho, and Y. Nojima, "Complexity, interpretability and explanation capability of fuzzy rule-based classifiers," Proc. of 2009 IEEE International Conference on Fuzzy Systems, pp. 1730-1735, Jeju Island, Korea, August 20-24, 2009.
  191. Y. Nojima and H. Ishibuchi, "Interactive fuzzy modeling by evolutionary multiobjective optimization with user preference," Proc. of 2009 IFSA World Congress and 2009 EUSFLAT Conference, pp. 1839-1844, Lisbon, Portugal, July 20-24, 2009.
  192. H. Ishibuchi and Y. Nojima, "Discussions on interpretability of fuzzy systems using simple examples," Proc. of 2009 IFSA World Congress and 2009 EUSFLAT Conference, pp. 1649-1654, Lisbon, Portugal, July 20-24, 2009.
  193. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Single-objective and multi-objective formulations of solution selection for hypervolume maximization," Proc. of 2009 Genetic and Evolutionary Computation Conference, pp. 1831-1832, Montreal, Canada, July 8-12, 2009.
  194. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Effects of using two neighborhood structures on the performance of cellular evolutionary algorithms for many-objective optimization," Proc. of 2009 IEEE Congress on Evolutionary Computation, pp. 2508-2515, Trondheim, Norway, May 18-21, 2009.
  195. H. Ishibuchi, N. Tsukamoto, Y. Sakane, and Y. Nojima, "Hypervolume approximation using achievement scalarizing functions for evolutionary many-objective optimization," Proc. of 2009 IEEE Congress on Evolutionary Computation, pp. 530-537, Trondheim, Norway, May 18-21, 2009.
  196. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Adaptation of scalarizing functions in MOEA/D: An adaptive scalarizing function-based multiobjective evolutionary algorithm," Proc. of 5th International Conference on Evolutionary Multi-Criterion Optimization, pp. 438-452, Nantes, France, April 7-10, 2009.
  197. Y. Nojima and H. Ishibuchi, "Interactive genetic fuzzy rule selection through evolutionary multiobjective optimization with user preference," Proc. of 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, pp. 141-148, Nashville, USA, March 30-April 2, 2009. [PDF]
  198. Y. Nojima, “Multi-objective behavior coordination based on sensory network for multiple mobile robots,” Proc. of 2009 IEEE Workshop on Robotic Intelligence in Informationally Structured Space, pp. 66-72, Nashville, USA, March 30-April 2, 2009.
  199. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Empirical analysis of using weighted sum fitness functions in NSGA-II for many-objective 0/1 knapsack problems," Proc. of 11th International Conference on Computer Modelling and Simulation (UKSim 2009), pp. 71-76, Cambridge, UK, March 25-27, 2009.
  200. Y. Nojima, Y. Hamada, and H. Ishibuchi, "Application of interactive fuzzy data mining to the analysis of inter-vehicle communication in traffic simulations," Proc. of 5th International Conference on Sciences of Electronic, Technologies of Information and Telecommunications, (CD-ROM 11 pages) Hammamet, Tunisia, March 22-26, 2009.
  201. Y. Nojima and H. Ishibuchi, "Incorporation of user preference into multiobjective genetic fuzzy rule selection for pattern classification problems," Proc. of 14th International Symposium on Artificial Life and Robotics, pp. 186-189, Oita, Japan, February 5-7, 2009.
  202. I. Kuwajima, Y. Nojima, and H. Ishibuchi, "Pareto-optimal fuzzy rule mining with EMO algorithms and its improvement by heuristic initialization," Proc. of 14th International Symposium on Artificial Life and Robotics, pp. 377-380, Oita, Japan, February 5-7, 2009.
  203. N. Tsukamoto, Y. Sakane, Y. Nojima, and H. Ishibuchi, "Hybridization of evolutionary multiobjective optimization algorithms by the adaptive use of scalarizing fitness function," Proc. of 14th International Symposium on Artificial Life and Robotics, pp. 365-368, Oita, Japan, February 5-7, 2009.
  204. Y. Hamada, Y. Nojima, and H. Ishibuchi, "Use of multiobjective genetic rule selection for examining the effectiveness of inter-vehicle communication in traffic simulations," Proc. of 14th International Symposium on Artificial Life and Robotics, pp. 93-96, Oita, Japan, February 5-7, 2009.
  205. H. Ohyanagi, Y. Wakamatsu, Y. Nakashima, Y. Nojima, and H. Ishibuchi, "Evolution of cooperative behavior among heterogeneous agents with different strategy representations in an iterated prisoner's dilemma game," Proc. of 14th International Symposium on Artificial Life and Robotics, pp. 102-105, Oita, Japan, February 5-7, 2009.

    206. 2008
  206. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Use of local ranking in cellular genetic algorithms with two neighborhood structures," Proc. of 7th International Conference on Simulated Evolution and Learning (SEAL 2008), pp. 309-318, Melbourne, Australia, December 7-10, 2008.
  207. H. Ishibuchi and Y. Nojima, "Evolutionary multiobjective fuzzy system design," Proc. of 2nd Workshop on Computing and Communications from Biological Systems: Theory and Applications, CD ROM Proceedings (2 pages), Awaji Island, Japan, November 28, 2008.
  208. Y. Nojima and H. Ishibuchi, "Effects of diversity measures on the design of ensemble classifiers by multiobjective genetic fuzzy rule selection with a multi-classifier coding scheme," Proc. of Third International Workshop on Hybrid Artificial Intelligence Systems, pp. 755-762, Burgos, Spain, September 24-26, 2008. [PDF]
  209. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Maintaining the diversity of solutions by non-geometric binary crossover in genetic algorithms," Proc. of Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on Advanced Intelligent Systems, pp. 1512-1517, Nagoya, Japan, September 17-21, 2008.
  210. H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, "Use of heuristic local search for single-objective optimization in multiobjective memetic algorithms," Proc. of 10th International Conference on Parallel Problem Solving from Nature, pp. 743-752, Dortmund, Germany, September 13-17, 2008.
  211. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Examining the effect of elitism in cellular genetic algorithms using two neighborhood structures," Proc. of 10th International Conference on Parallel Problem Solving from Nature, pp. 458-467, Dortmund, Germany, September 13-17, 2008.
  212. H. Ishibuchi, Y. Hitotsuyanagi, N. Tsukamoto, and Y. Nojima, "Probabilistic use of heuristic moves in multiobjective genetic local search for flowshop scheduling," Conference Handbook of the UK Operational Research Society 50th Annual Conference, p.115, York, UK, September 9-11, 2008.
  213. Y. Hitotsuyanagi, Y. Nojima, and H. Ishibuchi, "Balance between local search and global search in multiobjective memetic algorithms for many-objective optimization problems," Workshop and Summer School on Evolutionary Computing Lecture Series by Pioneers, pp. 22-25, Derry, Northern Ireland, August 18-22, 2008.
  214. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Maintaining the diversity of solutions by non-geometric binary crossover: A worst one-max solver competition case study," Proc. of 2008 Genetic and Evolutionary Computation Conference, pp. 1111-1112, Atlanta, Georgia, USA, July 12-16, 2008.
  215. H. Ishibuchi, N. Tsukamoto, Y. Hitotsuyanagi, and Y. Nojima, "Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems," Proc. of 2008 Genetic and Evolutionary Computation Conference, pp. 649-656, Atlanta, Georgia, USA, July 12-16, 2008.
  216. Y. Nojima, and H. Ishibuchi, "Computational efficiency of parallel distributed genetic fuzzy rule selection for large data sets," Proc. of Information Processing and Management of Uncertainty in Knowledge-based Systems, pp. 1137-1142, Torremolinos, Spain, June 22-27, 2008.
  217. I. Kuwajima, H. Ishibuchi, and Y. Nojima, "Effectiveness of designing fuzzy rule-based classifiers from Pareto-optimal rules," Proc. of 2008 IEEE International Conference on Fuzzy Systems, pp. 1185-1192, Hong Kong, June 1-6, 2008.
  218. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization: A short review," Proc. of 2008 IEEE Congress on Evolutionary Computation, pp. 2424-2431, Hong Kong, June 1-6, 2008.
  219. H. Ishibuchi, Y. Hitotsuyanagi, and Y. Nojima, "Scalability of multiobjective genetic local search to many-objective problems: Knapsack problem case studies," Proc. of 2008 IEEE Congress on Evolutionary Computation, pp. 3587-3594, Hong Kong, June 1-6, 2008.
  220. H. Ishibuchi, Y. Kaisho, and Y. Nojima, "A visual explanation system for explaining fuzzy reasoning results by fuzzy rule-based classifiers," Proc. of 2008 North American Fuzzy Information Processing Society Conference, in CD-ROM (6 pages), New York, USA, May 19-22, 2008.
  221. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Behavior of evolutionary many-objective optimization," Proc. of 10th International Conference on Computer Modeling and Simulation, pp. 266-271, Cambridge, UK, April 1-3, 2008.
  222. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization," Proc. of 3rd International Workshop on Genetic and Evolving Fuzzy Systems, pp. 47-52, Witten-Bommerholz, Germany, March 4-7, 2008.
  223. H. Ishibuchi, Y. Kaisho, and Y. Nojima, "Designing fuzzy rule-based classifiers that can visually explain their classification results to human users," Proc. of 3rd International Workshop on Genetic and Evolving Fuzzy Systems, pp. 5-10, Witten-Bommerholz, Germany, March 4-7, 2008. Best Paper Finalist
  224. N. Tsukamoto, Y. Nojima, and H. Ishibuchi, "Effects of non-geometric binary crossover on multiobjective 0/1 knapsack problems," Proc. of 13th International Symposium on Artificial Life and Robotics, pp. 642-645, Oita, Japan, January 31-February 2, 2008.
  225. I. Kuwajima, Y. Nojima, and H. Ishibuchi, "Effects of constructing fuzzy discretization from crisp discretization for rule-based classifiers," Proc. of 13th International Symposium on Artificial Life and Robotics, pp. 203-206, Oita, Japan, January 31-February 2, 2008.
  226. I. Kuwajima, Y. Nojima, and H. Ishibuchi, "Obtaining accurate classifiers with Pareto-optimal and near Pareto-optimal rules," Proc. of 13th International Symposium on Artificial Life and Robotics, pp. 195-198, Oita, Japan, January 31-February 2, 2008.

    227. 2007
  227. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Choosing extreme parents for diversity improvement in evolutionary multiobjective optimization algorithms," Proc. of 2007 IEEE International Conference on Systems, Man and Cybernetics, pp. 1946-1951, Montreal, Canada, October 7-10, 2007.
  228. K. Ohara, Y. Nojima, and H. Ishibuchi, "A study on traffic information sharing through inter-vehicle communication," Proc. of 2007 IEEE Multi-conference on Systems and Control, pp. 670-675, Singapore, October 1-3, 2007.
  229. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Iterative approach to indicator-based multiobjective optimization," Proc. of 2007 IEEE Congress on Evolutionary Computation, pp. 3697-3704, Singapore, September 25-28, 2007.
  230. H. Ishibuchi, Y. Hitotsuyanagi, and Y. Nojima, "An empirical study on the specification of the local search application probability in multiobjective memetic algorithms," Proc. of 2007 IEEE Congress on Evolutionary Computation, pp. 2788-2795, Singapore, September 25-28, 2007.
  231. K. Ohara, Y. Nojima, Y. Kitano, and H. Ishibuchi, "Effects of spatial structures on evolution of iterated prisoner's dilemma game strategies with probabilistic decision making," Proc. of 2007 IEEE Congress on Evolutionary Computation, pp. 4051-4058, Singapore, September 25-28, 2007.
  232. H. Ishibuchi, I. Kuwajima, and Y. Nojima, "Prescreening of candidate rules using association rule mining and Pareto-optimality in genetic rule selection," Proc. of 11th International Conference on Knowledge Based Intelligent Information and Engineering Systems, pp. 509-516, Vietri sul Mare, Italy, September 12-14, 2007.
  233. H. Ishibuchi, K. Ohara, and Y. Nojima, "Implementation of elitism in cellular genetic algorithms," Proc. of 8th International Symposium on Advanced Intelligent Systems, pp. 169-174, Sokcho, Korea, September 5-8, 2007.
  234. Y. Hamada, Y. Nojima, K. Ohara, and H. Ishibuchi, "A simulation study of route selection with inter-vehicle communication," Proc. of 8th International Symposium on Advanced Intelligent Systems, pp. 175-180, Sokcho, Korea, September 5-8, 2007.
  235. Y. Hitotsuyanagi, Y. Nojima, and Hisao Ishibuchi, "Effects of problem-specific local search schemes in a memetic EMO algorithm," Proc. of 8th International Symposium on Advanced Intelligent Systems, pp. 402-407, Sokcho, Korea, September 5-8, 2007.
  236. Y. Nojima, I. Kuwajima, and H. Ishibuchi, "Data set subdivision for parallel distributed implementation of genetic fuzzy rule selection," Proc. of 2007 IEEE International Conference on Fuzzy Systems, pp. 2006-2011, London, UK, July 23-26, 2007.
  237. H. Ishibuchi, Y. Nojima, N. Tsukamoto, and K. Ohara, "Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization," Proc. of 2007 Genetic and Evolutionary Computation Conference, vol. 1, pp. 829-836, London, UK, July 7-11, 2007.
  238. H. Ishibuchi, I. Kuwajima, and Y. Nojima, "Relation between Pareto-optimal fuzzy rules and Pareto-optimal fuzzy rule sets," Proc. of 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making, pp. 42-49, Honolulu, USA, April 1-5, 2007.
  239. H. Ishibuchi, and Y. Nojima, "Optimization of scalarizing functions through evolutionary multiobjective optimization," Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, pp. 51-65, Springer, Berlin, 2007.

    240. 2006
  240. Y. Nojima and H. Ishibuchi, "Designing fuzzy ensemble classifiers by evolutionary multiobjective optimization with an entropy-based diversity criterion," Proc. of 6th International Conference on Hybrid Intelligent Systems and 4th Conference on Neuro-Computing and Evolving Intelligence, (Auckland, New Zealand), in CD-ROM (4pages), (December 13-15, 2006). Best Paper Award
  241. H. Ishibuchi, Y. Nojima, and I. Kuwajima, "Finding simple fuzzy classification systems with high interpretability through multiobjective rule selection," Proc. of 10th International Conference on Knowledge Based Intelligent Information and Engineering Systems, (Bournemouth, UK), pp. 86-93, (October 9-11, 2006).
  242. H. Ishibuchi, Y. Nojima, and I. Kuwajima, "Accuracy-complexity tradeoff analysis in data mining by multiobjective genetic rule selection," Proc. of Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on Advanced Intelligent Systems, (Tokyo, Japan) pp. 2069-2074, (September 20-24, 2006).
  243. H. Ishibuchi, Y. Nojima, and T. Doi, "Driving evolutionary multiobjective search by a scalarizing fitness function," Proc. of Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on Advanced Intelligent Systems, (Tokyo, Japan), pp. 2198-2203, (September 20-24, 2006).
  244. H. Ishibuchi, I. Kuwajima, and Y. Nojima, "Multiobjective association rule mining," Proc. of PPSN Workshop on Multiobjective Problem Solving from Nature (September 9, 2006) 12 pages (Reykjavik, Iceland) http://dbkgroup.org/knowles/MPSN3/
  245. H. Ishibuchi, T. Doi, and Y. Nojima, "Effects of using two neighborhood structures in cellular genetic algorithms for function optimization," Proc. of 9th International Conference on Parallel Problem Solving from Nature (Reykjavik, Iceland) pp. 949-958 (September 9-13, 2006).
  246. H. Ishibuchi, T. Doi, and Y. Nojima, "Incorporation of scalarizing fitness functions into evolutionary multiobjective optimization algorithms," Proc. of 9th International Conference on Parallel Problem Solving from Nature (Reykjavik, Iceland) pp. 493-502 (September 9-13, 2006).
  247. H. Ishibuchi, Y. Nojima, and I. Kuwajima, "Genetic rule selection as a postprocessing procedure in fuzzy data mining," Proc. of 2006 International Symposium on Evolving Fuzzy Systems (Ambleside, Lake District, UK) pp. 286-291 (September 7-9, 2006). Best Runner-up paper award
  248. Y. Nojima, H. Ishibuchi, and I. Kuwajima, "Comparison of search ability between genetic fuzzy rule selection and fuzzy genetics-based machine learning," Proc. of 2006 International Symposium on Evolving Fuzzy Systems (Ambleside, Lake District, UK) pp. 125-130 (September 7-9, 2006).
  249. H. Ishibuchi, Y. Nojima, and I. Kuwajima, "Fuzzy data mining by heuristic rule extraction and multiobjective genetic rule selection," Proc. of 2006 IEEE International Conference on Fuzzy Systems (Vancouver, Canada) pp. 7824-7831 (July 16-21, 2006).
  250. H. Ishibuchi, Y. Nojima, and T. Doi, "Comparison between single-objective and multi-objective genetic algorithms: Performance comparison and performance measures," Proc. of 2006 Congress on Evolutionary Computation (Vancouver, Canada) pp. 3959-3966 (July 16-21, 2006).
  251. H. Ishibuchi, Y. Nojima, and I. Kuwajima, "Multiobjective genetic rule selection as a data mining postprocessing procedure," Proc. of 2006 Genetic and Evolutionary Computation Conference (Seattle, USA) volume 2, pp. 1591-1592 (July 8-12, 2006).
  252. H. Ishibuchi, Y. Nojima, K. Narukawa, and T. Doi, "Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms," Proc. of 2006 Genetic and Evolutionary Computation Conference (Seattle, USA) volume 1, pp. 741-742 (July 8-12, 2006).
  253. K. Ohara, Y. Nojima, and H. Ishibuchi, "Comparison between centralized global optimization and distributed local optimization for traffic jam avoidance," Proc. of 2006 Genetic and Evolutionary Computation Conference Late Breaking Papers (Seattle, USA) 6p CD ROM (July 8-12, 2006).
  254. H. Ishibuchi and Y. Nojima, "Tradeoff between accuracy and rule length in fuzzy rule-based classification systems for high-dimensional problems," Proc. of 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, (Paris, France) pp. 1936-1943 (July 2-7, 2006).
  255. H. Ishibuchi, Y. Nojima, and T. Doi, "Application of multiobjective evolutionary algorithms to single-objective optimization problems," Abstract Booklet of 7th International Conference on Multi-Objective Programming and Goal Programming, (Tours, France) 4 pages (June 12-14, 2006).
  256. Y. Nojima and H. Ishibuchi, "Interpretability-accuracy tradeoff by multiobjective genetics-based machine learning for pattern classification problems," Proc. of The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06), pp. 452-455, B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006.

    257. 2005
  257. H. Ishibuchi and Y. Nojima, "Performance evaluation of evolutionary multiobjective approaches to the design of fuzzy rule-based ensemble classifiers," Proc. of 5th International Conference on Hybrid Intelligent Systems (Rio de Janeiro, Brazil) pp. 271-276 (November 6-9, 2005).
  258. K. Narukawa, Y. Nojima, and H. Ishibuchi, "Effects of similarity-based mating scheme on evolutionary function optimization," Proc. of 6th International Symposium on Advanced Intelligent Systems, pp. 735-740, (2005). Outstanding Paper Award
  259. H. Ishibuchi and Y. Nojima, "Multiobjective formulations of fuzzy rule-based classification system design," Proc. of Fourth Conference of the European Society for Fuzzy Logic and Technology and 11 Rencontres Francophones sur la Logique Floue et ses Applications (EUSFLAT-LFA 2005), pp. 285-290, Barcelona, Spain, September 7-9, (2005)
  260. H. Ishibuchi, K. Narukawa, and Y. Nojima, "An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization," Proc. of 2005 Genetic and Evolutionary Computation Conference, vol. 1, pp. 817-824, Washington DC, USA, June 25-29, (2005)
  261. H. Ishibuchi and Y. Nojima, "Comparison between fuzzy and interval partitions in evolutionary multiobjective design of rule-based classification systems," Proc. of 2005 IEEE International Conference on Fuzzy Systems, pp. 430-435, Reno, USA, May 22-25, (2005) [PDF]
  262. K. Narukawa, Y. Nojima, and H. Ishibuchi, "Modification of evolutionary multiobejective optimization algorithms for multiobjective design of fuzzy rule-based classification systems," Proc. of 2005 IEEE International Conference on Fuzzy Systems, pp. 809-814, Reno, USA, May 22-25, (2005)
  263. H. Ishibuchi and Y. Nojima, "Multiobjective fuzzy genetics-based machine learning," Proc. of the 1st Workshop on Genetic Fuzzy Systems (GFS05), pp. 10-15, Granada, Spain, March 17-19, (2005)
  264. Y. Nojima, K. Narukawa, S. Kaige, and H. Ishibuchi, "Effects of removing overlapping solutions on the performance of the NSGA-II algorithm," Proc. of the third International Conference on Evolutionary Multi-Criterion Optimization (EMO2005), (C.A. Coello Coello, A. H. Aguirre, E. Zitzler Eds, LNCS 3410), pp. 341-354 Guanajuato, Mexico, March 9-11, (2005)
  265. S. Namba, Y. Nojima, and H. Ishibuchi, "Performance comparison between fuzzy rules and interval rules in rule-based classification systems," Proc. of the 10th International Symposium on Artificial Life and Robotics (AROB05), CDROM (4 pages), Oita, Japan, February 4-6, (2005)
  266. Y. Nojima, N. Kubota, and F. Kojima, "Gesture clustering and imitative behavior generation for partner robots," Proc. of the 10th International Symposium on Artificial Life and Robotics (AROB05), CDROM (4 pages), Oita, Japan, February 4-6, (2005) [PDF]

    267. 2004
  267. N. Kubota, Y. Nojima, and F. Kojima, "Imitative behavior generation for a vision-based partner robot," Proc. of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2004), pp. 3080-3085, Sendai, Japan, September 28 - October 2, (2004) [PDF]
  268. Y. Nojima, N. Kubota, and F. Kojima, "Trajectory generation and accumulation for partner robots based on structured learning," Proc. of 2004 IEEE Congress on Evolutionary Computation (CEC2004), pp. 2224-2229, Portland, Oregon, June 19-23, (2004)

    269. 2003
  269. N. Kubota, Y. Nojima, I. Sulistijono, and F. Kojima, "Interactive trajectory generation using evolutionary programming for a partner robot," Proc. of the 12th IEEE Workshop Robot and Human Interactive Communication RO-MAN 2003, pp. 335-340, San Francisco, USA, October 31-November 2 (2003)
  270. Y. Nojima, F. Kojima, and N. Kubota, "Trajectory generation for human-friendly behavior of partner robot using fuzzy evaluating interactive genetic algorithm," Proc. of IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA2003), pp. 306-311 in CD-ROM, Kobe, Japan, July (2003)
  271. Y. Nojima, F. Kojima, and N. Kubota, "Local episode-based learning of multi-objective behavior coordination for a mobile robot in dynamic environments," Proc. of 2003 IEEE International Conference on Fuzzy Systems, pp. 307-312 in CD-ROM, St.Louis, USA, May (2003)

    272. 2002
  272. Y. Nojima, F. Kojima, and N. Kubota, "Local episode-based learning of a mobile robot in a dynamic environment," Proc. of The Third International Symposium on Human and Artificial Intelligence Systems, pp. 384-388, Fukui, Japan, December 6-7 (2002)

    273. 2001
  273. Y. Nojima, F. Kojima, and N. Kubota, "Perception and behavior of pet robots based on emotional model," Proc. of Knowledge Based Intelligent Information Engineering System & Allied Technologies (KES2001), pp. 859-863, Osaka, Japan, September 6-8 (2001)
  274. N. Kubota, Y. Nojima, F. Kojima, and T. Fukuda, "Dual learning for perception and behavior of mobile robots," Proc. of Joint 9th IFSA World Congress and 20th NAFIPS International Conference (IFSA/NAFIPS2001), pp. 1401-1406, Vancouver, Canada, July 25-28 (2001)

    275. 2000
  275. N. Kubota, Y. Nojima, F. Kojima, and T. Fukuda, "Multi-objective behavior coordinate for a mobile robot with fuzzy neural networks," Proc. of The IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN2000), CD-ROM Proc., Como, Italy, July 24-27 (2000)
  276. N. Kubota, Y. Nojima, N. Baba, F. Kojima, and T. Fukuda, "Evolving pet robot with emotional model," Proc. of 2000 IEEE Congress on Evolutionary Computation (CEC2000), CD-ROM Proc. pp. 1231-1237, San Diego, USA, July 16-19 (2000)
  277. Y. Nojima, N. Kubota, F. Kojima, and T. Fukuda, "Control of behavior dimension for mobile robots," Proc. of The Forth Asian Fuzzy Systems Symposium (AFFS2000), pp. 652-657, Tsukuba, Japan, May 31-June 3 (2000)

    278. 1999
  278. N. Kubota, Y. Nojima, F. Kojima, T. Fukuda, and S. Shibata, "Intelligent control of self-organiziing manufacturing system with local learning mechanism," Proc. of The 25th Annual Conference of the IEEE Industrial Electronics Society (IECON'99), CD-ROM Proc. (6 page), San Jose, USA, November 29-December 3 (1999)

Editorial Works:
  1. Z. Deng, J. Lu, D. Wu, K.-S. Choi, S. Sun, and Y. Nojima, "Guest editorial: Sepecial issue on new advances in deep-transfer learning," IEEE Trans. on Emerging Topics in Computational Intelligence, vol. 3, no. 5, pp. 357-359, Oct. 2019.
  2. C.-H. Chen, C.-K. Ting, and Y. Nojima, “Soft computing for big data and social informatics,” Soft Computing, vol. 21, no. 11, pp. 2799-2800, June 2017.
  3. R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, "Special issue on "Evolutionary fuzzy systems" EFSs," Knowledge-Based Systems, vol. 54, pp. 1-2, December 2013.
  4. R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, “Special issue on evolutionary fuzzy systems: Guest editors’ introduction,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 20, no. 2, pp. v-x, October 2012.
  5. R. Alcala, Y. Nojima, H. Ishibuchi, and F. Herrera, "Special issue on evolutionary fuzzy systems," International Journal of Computational Intelligence Systems, vol. 5, no. 2, pp. 209-211, April 2012.
  6. Y. Nojima and M. Koeppen, "Special issue on selected papers from NaBIC 2010," Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 15, no. 9, p. 1299, November 2011.
  7. Y. Nojima, R. Alcala, H. Ishibuchi, and F. Herrera, "Special issue on evolutionary fuzzy systems," Soft Computing, vol. 15, no. 12, pp. 2299-2301, November 2011.
  8. R. Alcala and Y. Nojima, "Special issue on genetic fuzzy systems: New advances," Evolutionary Intelligence, vol. 2, no. 1-2, pp. 1-3, November 2009

Activities



Executive/Board Members Chair/Co-chairs Special Session Organizers Program Committee Members Editorial Boards Reviewed Journals Other Committees