能島裕介（Yusuke Nojima），大阪公立大学大学院情報学研究科教授

能島 裕介 Yusuke Nojima

教授・副研究科長

計算知能研究室
基幹情報学専攻 大学院情報学研究科 大阪公立大学

研究概要 / Research Overview

能島裕介（Yusuke Nojima）は，大阪公立大学大学院情報学研究科・基幹情報学専攻の教授です． 計算知能，ファジィシステム，進化計算，多目的最適化，進化型機械学習，ファジィ遺伝的機械学習， 説明可能AI，解釈可能AI，公平性AI，信頼できるAIを中心に研究しています． 代表的な研究テーマには，進化型多目的最適化によるファジィ識別器設計，遺伝的ファジィシステム， MAP-ElitesなどのQuality-Diversityアルゴリズム，MOEA/D，適応共鳴理論に基づくクラスタリング， 実世界最適化問題，魚類生息環境評価における解釈可能AIの応用が含まれます．

Research interests include computational intelligence, fuzzy systems, evolutionary computation, multiobjective optimization, evolutionary machine learning, fuzzy genetics-based machine learning, explainable and interpretable AI, fairness-aware AI, reliable AI, MAP-Elites, quality-diversity algorithms, MOEA/D, adaptive resonance theory-based clustering, real-world optimization, and interpretable AI for fish habitat assessment.

研究キーワード: 計算知能，ファジィシステム，遺伝的ファジィシステム，進化計算，多目的最適化， 進化型多目的最適化，ファジィ遺伝的機械学習，説明可能AI，公平性AI，信頼できるAI， MAP-Elites，Quality-Diversity，MOEA/D

E-mail: nojima (at) omu.ac.jp
599-8531 大阪府堺市中区学園町1-1
Phone: 072-254-9350 FAX: 072-254-9825

Google Scholar
dblp computer science bibliography

New Info


Biography


1976年7月：富山生まれ
1999年3月：大阪工業大学 工学部 機械工学科　卒業
2001年3月：大阪工業大学 大学院工学研究科 機械工学専攻 博士前期課程　修了
2004年3月：神戸大学 大学院自然科学研究科 システム機能科学専攻 博士後期課程　修了
博士（工学）
2004年4月：大阪府立大学 大学院工学研究科　助手
2007年4月：大阪府立大学 大学院工学研究科　助教
2013年4月：大阪府立大学 大学院工学研究科　准教授
2020年10月：大阪府立大学 大学院工学研究科　教授
2022年4月：大阪公立大学 大学院情報学研究科　教授
2026年4月：大阪公立大学 大学院情報学研究科　副研究科長
- 海外滞在歴 -
2000年8-10月：University of South Australia (invited by Prof. L.C. Jain, Dr. D. Corbett)
2008年7-9月：University of Granada (invited by Prof. F. Herrera, Dr. R. Alcala)
2011年7月（1ヶ月）：University of Granada (invited by Prof. F. Herrera, Dr. R. Alcala)


受賞歴

Publications


著書（編著）
  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.

著書分担執筆
  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. 能島裕介，久保田直行，ファジィ制御を用いた多目的行動の進化的学習，進化技術ハンドブック，第II巻，電気学会進化技術応用調査専門委員会編，近代科学社，23.2節，pp. 453-459, 2011.
  5. 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.
  6. 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), Feburary 2009.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. H. Ishibuchi and Y. Nojima, "Fuzzy Ensemble Design through Multiobjective Fuzzy Rule Selection," In Y. Jin (ed.): Multi-Objective Machine Learning, Springer, Berlin, pp. 507-530, 2006.
  13. Y. Nojima, F. Kojima, and 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.

論文
  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. 高﨑志遠, 福田信二, 増山直輝, 能島裕介, 多重共線性に基づく属性選択を用いた多目的ファジィ遺伝的機械学習による魚類生息環境評価, 日本知能情報ファジィ学会誌, vol 37, no. 1, pp. 524-529, Feb. 2025.
  9. 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.
  10. 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.
  11. 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.
  12. 小西豪, 増山直輝, 能島裕介, 2段階ファジィ遺伝的機械学習におけるアーカイブ個体群利用の効果検証, 日本知能情報ファジィ学会誌, vol. 36, no. 1, pp. 565-570, Feb. 2024．
  13. 西川毅, 増山直輝, 能島裕介, マルチラベル量質混在データを対象とした適応共鳴理論に基づくクラスタリング手法による識別器の改良, 日本知能情報ファジィ学会誌, vol. 36, no. 1, pp. 543-549, Feb. 2024．
  14. 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
  15. 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
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 濱田直希，於保俊，谷垣勇輝，原田智広，能島裕介，"ゲーム内イベントに対する認知的バイアスを考慮した乱数生成問題 - 進化計算コンペティション2020の結果報告," 進化計算学会論文誌, vol. 12, no. 3, 112-124, 2021.
  29. 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.
  30. 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.
  31. 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.
  32. 西原光洋, 増山直輝, 能島裕介, 石渕久生, クラス不均衡データに対するミシガン型ファジィ遺伝的機械学習, 日本知能情報ファジィ学会誌, vol. 33, no. 1, pp. 525-530, 2021．
  33. 面崎祐一, 増山直輝, 能島裕介, 石渕久生, マルチラベル多目的ファジィ遺伝的機械学習の多数目的最適化への拡張, 日本知能情報ファジィ学会誌, vol. 33, no. 1, pp. 531-536, 2021．
  34. 藤井祐人, 増山直輝, 能島裕介, 石渕久生, 2目的問題に変換する分解ベース進化型マルチモーダル多目的最適化アルゴリズム, 日本知能情報ファジィ学会誌, vol. 33, no. 1, pp. 537-542, 2021．
  35. 増山直輝, 坪田一希, 能島裕介, 石渕久生, クラス別FTCAに基づく識別器設計, 日本知能情報ファジィ学会誌, vol. 33, no. 1, pp. 543-548, 2021．
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 橋本龍一, 増山直輝, 能島裕介, 石渕久生, 進化型多目的マルチタスク最適化手法におけるタスク間交叉時の親個体が探索性能に与える影響, 日本知能情報ファジィ学会誌, vol. 32, no. 1, pp. 501-506, 2020.
  41. 入江勇斗, 増山直輝, 能島裕介, 石渕久生, 未知クラスの継続的な学習を可能とするファジィ遺伝的機械学習手法, 日本知能情報ファジィ学会誌, vol. 32, no. 1, pp. 512-517, 2020.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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
  52. 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.
  53. 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)
  54. 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.
  55. 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.
  56. 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
  57. 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!
  58. 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.
  59. 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]
  60. 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.
  61. 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.
  62. 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.
  63. 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.
  64. 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.
  65. 一柳徳宏，若松良彦，能島裕介，石渕久生：多目的遺伝的局所探索アルゴリズムにおける局所探索適用個体の選択，システム制御情報学会論文誌, vol. 23, no. 8, pp. 178-187 (2010年8月).
  66. 塚本実孝，坂根悠治，能島裕介，石渕久生：Indicatorに基づく進化型多目的最適化アルゴリズムへのHypervolume近似手法の適用，システム制御情報学会論文誌, vol. 23, no. 8, pp. 165-177 (2010年8月).
  67. 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.
  68. 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]
  69. 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.
  70. 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.
  71. 塚本実孝，坂根悠治，能島裕介，石渕久生：進化型多目的最適化に対するスカラー化関数を用いたHypervolumeの近似手法の提案，システム制御情報学会論文誌, vol. 22, no. 11, pp. 385-395 (2009年11月). 学会賞論文賞
  72. 塚本実孝，能島裕介，石渕久生：多数目的最適化問題における進化型多目的最適化アルゴリズムの問題点とその改良手法に関する考察，システム制御情報学会論文誌, vol. 22, no. 6, pp. 220-228 (2009年6月).
  73. 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.
  74. 桑島功，能島裕介，石渕久生，パレート最適ルールを候補ルールとして用いる遺伝的ファジィルール選択，日本知能情報ファジィ学会誌, Vol. 20, No.2, pp. 231-243, 2008.
  75. Y. Nojima, H. Ishibuchi, and I. Kuwajima, "Parallel distributed genetic fuzzy rule selection," Soft Computing, vol. 13, no. 5, pp. 511-519, March 2009.
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. 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]
  81. 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.
  82. 大原健，能島裕介，石渕久生：交通渋滞解消のための大域的及び局所的最適化経路選択手法の性能調査，日本知能情報ファジィ学会誌, Vol. 18, No.6, pp. 867-873, 2006.
  83. 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.
  84. 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.
  85. 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.
  86. 能島裕介，小島史男，久保田直行，多目的行動調停に基づく移動ロボットの行動獲得，日本機械学会論文集C編，68巻671号，pp. 2067-2073, 7月 (2002).
  87. 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.
  88. 久保田直行，能島裕介，小島史男，福田敏男，柴田晋，学習機構を持つ自在搬送システムの最適化，日本機械学会論文集C編，66巻652号，pp. 3970-3976, 12月 (2000).

解説・エッセイ・報告
  1. 能島裕介, "ファジィ遺伝的機械学習," 知能と情報, vol. 36, no. 1, p. 47, Feb. 2024.
  2. 能島裕介, 高木英行, 棟朝雅晴, 濱田直希, 西原慧, 高玉圭樹, 佐藤寛之, 桐淵大貴, 宮川みなみ, "オープンスペースディスカッション2021実施報告," 進化計算学会論文誌, vol. 13, no. 1, pp. 1-9, 2022.
  3. 濱田直希，穴井宏和，梅田裕平，千葉一永，佐藤寛之，能島裕介，加葉田雄太朗，一木俊助，早野健太，佐伯修, "2020年度採択分 九州大学マス・フォア・インダストリ研究所 共同利用研究集会 進化計算の数理," MI Lecture Note Series, Volume No.:86, ISSN:2188-1200, 2022年2月
  4. C.-S. Lee, M.-H. Wang, L.-W. Ko, Y.-H. Lee, H. Ohashi, N. Kubota, Y. Nojima, and S.-F. Su, “Human intelligence meets smart machine at IEEE SMC 2018,” IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 1, pp. 23-31, Jan. 2020.
  5. 本多克広，能島裕介，中島智晴，"SOFT-CR連携ファジィ学問塾2019開催報告," 知能と情報，vol. 31, no. 6, pp. 10-11, 2019.
  6. 能島裕介，Joint 17th IFSA World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems開催報告，知能と情報，vol. 29, no. 5, pp. 173-174, 2017.
  7. 能島裕介，遺伝的機械学習による多目的知識獲得，科研費NEWS, vol. 1, p. 14, 2016.
  8. 能島裕介，石渕久生，遺伝的機械学習を用いた大規模データからの知識獲得，システム／制御／情報，第57巻10号，pp. 421-426, 2013.
  9. 能島裕介，進化技術ハンドブック：第I巻基礎編，知能と情報，vol. 23, no. 1, p. 120, 2011.
  10. 能島裕介，FUZZ-IEEE 2005参加報告，知能と情報，vol. 17, no. 5, pp. 607-608, 2005.

国際会議論文

    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.

    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. N. Masuyama, Y. Nojima, C. K. Loo, and H. Ishibuchi, "Multi-label classification based on adaptive resonance theory," Proc. of 2020 IEEE Symposium Series on Computational Intelligence (SSCI 2020), pp. 1913-1920, Canberra, Australia, December 1-4, 2020.
  44. 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), 6 pages, Tokyo, Japan, September 23-26, 2020.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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
  50. 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.
  51. 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]
  52. 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.

    53. 2019
  53. 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.
  54. 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.
  55. 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.
  56. 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.
  57. 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.
  58. 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.
  59. 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.
  60. 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.
  61. 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.
  62. 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]
  63. 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

    64. 2018
  64. 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.
  65. 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]
  66. T. Matsumoto, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Performance comparison of multiobjective evolutionary algorithms on problems with partially different propeties from popular scalable test suites," Proc. of 2018 IEEE International Conference on Systems, Man, and Cybernetics, Miyazaki, Japan, Oct. 7-10, 2018 (Accepted).
  67. 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, Coimbra, Portugal, Sep. 8-12, 2018 (Accepted).
  68. 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, Coimbra, Portugal, Sep. 8-12, 2018 (Accepted).
  69. 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, Coimbra, Portugal, Sep. 8-12, 2018 (Accepted).
  70. 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 (Accepted).
  71. 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.
  72. 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.
  73. 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.
  74. 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.

    75. 2017
  75. 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.
  76. 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.
  77. 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.
  78. 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]
  79. 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]
  80. 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]
  81. 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.
  82. 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.
  83. 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]
  84. 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]

    85. 2016
  85. 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.
  86. 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.
  87. 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.
  88. 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.
  89. 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.
  90. 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.
  91. 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.
  92. 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.
  93. 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.
  94. 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]
  95. 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.
  96. 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.
  97. 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.
  98. 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.
  99. 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.
  100. 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.

    101. 2015
  101. 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.
  102. 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
  103. 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]
  104. 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.
  105. 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.
  106. 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.
  107. 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.
  108. 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.
  109. 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.
  110. 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]
  111. 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.
  112. 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.
  113. 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

    114. 2014
  114. 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.
  115. 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.
  116. 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.
  117. 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.
  118. 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.
  119. 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.
  120. 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.
  121. 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.
  122. 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.
  123. 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.
  124. 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.
  125. 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.
  126. 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.
  127. 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.

    128. 2013
  128. 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.
  129. 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
  130. 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.
  131. 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.
  132. 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.
  133. 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.
  134. 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.
  135. 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.
  136. 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.
  137. 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.
  138. 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]
  139. 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.
  140. 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.
  141. 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.
  142. 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.
  143. 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.
  144. 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.

    145. 2012
  145. 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.
  146. 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.
  147. 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.
  148. 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.
  149. 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.
  150. 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.
  151. 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).
  152. 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]
  153. 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.
  154. 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.

    155. 2011
  155. 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.
  156. 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.
  157. 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.
  158. 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.
  159. 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.
  160. 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.
  161. 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.
  162. 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
  163. 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.
  164. 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
  165. 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.
  166. 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.
  167. 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.

    168. 2010
  168. 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.
  169. 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.
  170. 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
  171. 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]
  172. 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]
  173. 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
  174. 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.
  175. 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.
  176. 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]
  177. 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]
  178. 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.
  179. 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.
  180. 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.
  181. 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]
  182. 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.

    183. 2009
  183. 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.
  184. 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.
  185. 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]
  186. 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.
  187. 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.
  188. 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
  189. 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.
  190. 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.
  191. 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.
  192. 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.
  193. 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.
  194. 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.
  195. 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.
  196. 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.
  197. 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.
  198. 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]
  199. 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.
  200. 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.
  201. 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.
  202. 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.
  203. 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.
  204. 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.
  205. 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.
  206. 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.

    207. 2008
  207. 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.
  208. 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.
  209. 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]
  210. 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.
  211. 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.
  212. 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.
  213. 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.
  214. 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.
  215. 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.
  216. 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.
  217. 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.
  218. 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.
  219. 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.
  220. 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.
  221. 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.
  222. 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.
  223. 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.
  224. 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
  225. 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.
  226. 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.
  227. 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.

    228. 2007
  228. 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.
  229. 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.
  230. 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.
  231. 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.
  232. 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.
  233. 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.
  234. 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.
  235. 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.
  236. 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.
  237. 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.
  238. 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.
  239. 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.
  240. 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.

    241. 2006
  241. 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
  242. 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).
  243. 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).
  244. 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).
  245. 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/
  246. 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).
  247. 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).
  248. 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
  249. 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).
  250. 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).
  251. 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).
  252. 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).
  253. 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).
  254. 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).
  255. 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).
  256. 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).
  257. 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.

    258. 2005
  258. 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).
  259. 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
  260. 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)
  261. 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)
  262. H. Ishibuchi and Y. Nojima, "Comparison between fuzzy and interval partitions in evolutionary multiobjective design of rule-based classification systems," Proc. of The IEEE international conference on fuzzy systems (FuzzIEEE2005), pp. 430-435, Reno, USA, May 22-25, (2005)
  263. K. Narukawa, Y. Nojima, and H. Ishibuchi, "Modification of evolutionary multiobejective optimization algorithms for multiobjective design of fuzzy rule-based classification systems," Proc. of The IEEE international conference on fuzzy systems (FuzzIEEE2005), pp. 809-814, Reno, USA, May 22-25, (2005)
  264. 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)
  265. 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)
  266. 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)
  267. 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)

    268. 2004
  268. 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)
  269. Y. Nojima, N. Kubota, and F. Kojima, "Trajectory generation and accumulation for partner robots based on structured learning," Proc. of 2004 Congress on Evolutionary Computation (CEC2004), pp. 2224-2229, Portland, Oregon, June 19-23, (2004)

    270. 2003
  270. 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)
  271. Y. Nojima, F. Kojima, and N. Kubota, "Trajectory generation for human-friendly behavior of partner robot using fuzzy evaluating interactive genetic algorithm," Proc. of The IEEE international Symposium on Computational Intelligence in Robotics and Automation (CIRA2003), pp. 306-311 in CD-ROM, Kobe, Japan, July (2003)
  272. 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 The IEEE international conference on fuzzy systems (FuzzIEEE2003), pp. 307-312 in CD-ROM, St.Louis, USA, May (2003)

    273. 2002
  273. 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)

    274. 2001
  274. 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)
  275. 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)

    276. 2000
  276. 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)
  277. N. Kubota, Y. Nojima, N. Baba, F. Kojima, and T. Fukuda, "Evolving pet robot with emotional model," Proc. of Congress on Evolutionary Computation 2000 (CEC2000), CD-ROM Proc. pp. 1231-1237, San Diego, USA, July 16-19 (2000)
  278. 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)

    279. 1999
  279. 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)

国内発表

    2026
  1. 小野田真子，徳坂光彦，増山直輝，能島裕介，3目的変換に基づく制約付きマルチモーダル多目的最適化アルゴリズムにおける親個体選択戦略の分析，第29回進化計算研究会講演論文集，東京，3月，(2026)．
  2. 小西豪，増山直輝，能島裕介，多目的ファジィ遺伝的機械学習による精度と個人公平性の関係解析，第29回進化計算研究会講演論文集，東京，3月，(2026)．

    3. 2025
  3. 徳坂光彦，増山直輝，能島裕介，3目的変換に基づく制約付きマルチモーダル多目的進化アルゴリズム，進化計算シンポジウム2025講演論文集，富山，12月，(2025)．
  4. 小西豪，増山直輝，能島裕介，グローバル解釈可能性及びローカル解釈可能性を考慮したMAP-Elitesに基づくファジィ識別器設計，進化計算シンポジウム2025講演論文集，富山，12月，(2025)．
  5. 小野錬司，能島裕介，増山直輝，制約付き多目的最適化問題における解集合からの多目的知識獲得とクラスタリング手法によるデータ削減効果，第28回進化計算研究会講演論文集，大阪，9月，(2025)．
  6. 熊谷至穏，能島裕介，増山直輝，少数派ラベルデータの追加生成によるマルチラベル識別器の性能向上，第41回ファジィシステムシンポジウム講演論文集，熊本，9月，(2025)．
  7. 髙﨑志遠，福田信二，増山直輝，能島裕介，多目的ファジィ遺伝的機械学習を用いた灌漑期・非灌漑期における魚類の生息場適性解析，第41回ファジィシステムシンポジウム講演論文集，熊本，9月，(2025)．
  8. 友藤彩人，Eric Vernon，増山直輝，能島裕介，解釈可能なハイブリッドモデルへのファジィ識別器の適用，第41回ファジィシステムシンポジウム講演論文集，熊本，9月，(2025)．
  9. 井上将旭，増山直輝，能島裕介，ARTMAPのための適応的警戒パラメータ調整，第41回ファジィシステムシンポジウム講演論文集，熊本，9月，(2025)．
  10. 伊瀬顕史，馬野元秀，増山直輝，能島裕介，ファジィ関係マップによるごみ焼却発電施設の燃焼安定度の解析―時間遅れデータを用いたマップの作成と利用―，第41回ファジィシステムシンポジウム講演論文集，熊本，9月，(2025)．
  11. 東祐輝，木下貴登，増山直輝，能島裕介，実世界制約付き多目的最適化問題における局所制約構造の解析，第41回ファジィシステムシンポジウム講演論文集，熊本，9月，(2025)．
  12. 徳坂光彦，増山直輝，能島裕介，2目的変換マルチモーダル多目的進化計算アルゴリズムの制約付き問題への拡張，第41回ファジィシステムシンポジウム講演論文集，熊本，9月，(2025)．
  13. 井上将旭，増山直輝，能島裕介，適応的警戒パラメータ制御機構を備えたARTMAP，第9回継続学習と知能の創発研究会講演論文集，大阪，7月，(2025)．
  14. 熊谷至穏，能島裕介，増山直輝，不均衡データの補正によるマルチラベル識別器の性能向上，第9回継続学習と知能の創発研究会講演論文集，大阪，7月，(2025)．
  15. 小西豪，増山直輝，能島裕介，品質多様性アルゴリズムによる解釈可能性と公平性を考慮したファジィ識別器設計の検討，2025年度人工知能学会全国大会論文集，大阪，5月，(2025)．
  16. 西川毅，増山直輝，能島裕介，適応共鳴理論に基づくクラスタリングによるマルチラベル識別器の不均衡データへの対応，第8回継続学習と知能の創発研究会講演論文集，東京，12月，(2025)．
  17. 田代一貴，増山直輝，能島裕介，X-meansによるノード統合機能を導入した成長型階層的クラスタリング手法の提案，第8回継続学習と知能の創発研究会講演論文集，東京，12月，(2025)．

    18. 2024
  18. 小篠豊志央，木下貴登，成山達也，三宅寿英，増山直輝，能島裕介，進化型多目的最適化アルゴリズムを用いた橋梁部材配置最適化，第18回進化計算シンポジウム2024講演論文集，pp. 47-51，白浜，12月（2024）
  19. Ruwang Jiao, Yusuke Nojima, Naoki Masuyama, A filter-based performance predictor for multi-objective wrapper feature selection in classification, 第18回進化計算シンポジウム2024（ポスターのみ），白浜，12月（2024）
  20. 木下貴登，増山直輝，能島裕介，異なる目的を持つ複数のクライアントに対する連合データ駆動多目的最適化アルゴリズムの検討，第18回進化計算シンポジウム2024講演論文集，pp. 133-136，白浜，12月（2024）
  21. 小西豪，増山直輝，能島裕介，ファジィ識別器における精度と公平性のトレードオフ解析，第18回進化計算シンポジウム2024（ポスターのみ），白浜，12月（2024）
  22. 東祐輝，増山直輝，能島裕介，分割型制約付き進化型多目的最適化アルゴリズムにおける重みベクトル調整量の探索への影響，第26回進化計算学会研究会講演論文集，pp. 83-87，大阪，9月，(2024)．
  23. 小西豪，増山直輝，能島裕介，MAP-Elites を用いたファジィ識別器設計の検討，第40回ファジィシステムシンポジウム講演論文集, 名古屋, 9月, (2024)．
  24. 藤井亮輔，増山直輝，能島裕介，継続学習におけるリプレイバッファのためのデータ選択手法の比較検討，第40回ファジィシステムシンポジウム講演論文集, 名古屋, 9月, (2024)．
  25. 高崎志遠，福田信二，増山直輝，能島裕介，多目的ファジィ遺伝的機械学習を用いた魚類の生息環境評価における属性選択の影響，第40回ファジィシステムシンポジウム講演論文集, 名古屋, 9月, (2024)．
  26. 島原基，増山直輝，能島裕介，閾値に基づく棄却オプションを導入したファジィ識別器における閾値最適化手法の検討，第40回ファジィシステムシンポジウム講演論文集, 名古屋, 9月, (2024)．
  27. 徳坂光彦，増山直輝，能島裕介，親個体選択戦略の変更による2目的変換に基づくマルチモーダル多目的最適化アルゴリズムの改良，第25回進化計算学会研究会講演論文集，pp. 179-189，横浜，3月 (2024).

    28. 2023
  28. 木下貴登，増山直輝，能島裕介，石渕久生，ε-局所差分プライバシを用いた連合サロゲート進化型多目的最適化フレームワークの検討，第17回進化計算シンポジウム2023講演論文集，pp. 325-329，小田原，12月（2023）
  29. 木下貴登，増山直輝，能島裕介，実世界多目的最適化問題のためのRiesz discrete s-Energy によるConvergence-Diversity Diagramの拡張，第24回進化計算学会研究会講演論文集，pp. 49-56，福知山，9月 (2023).
  30. ベーノンエリック，増山直輝，能島裕介，Overview of techniques for rule extraction from neural networks，第39回ファジィシステムシンポジウム講演論文集, 軽井沢, 9月, (2023)．
  31. 木下貴登，増山直輝，能島裕介，制約付き問題のための適応的問題分割ベース進化型多目的最適化アルゴリズムの検討s，第39回ファジィシステムシンポジウム講演論文集, 軽井沢, 9月, (2023)．
  32. 上田裕也，増山直輝，能島裕介，ε-局所差分プライバシを考慮した適応共鳴理論に基づく連合クラスタリング手法の検討，第39回ファジィシステムシンポジウム講演論文集, 軽井沢, 9月, (2023)．
  33. 西川毅，増山直輝，能島裕介，適応共鳴理論に基づくクラスタリングによるマルチラベル識別器の量質混在データへの対応，第39回ファジィシステムシンポジウム講演論文集, 軽井沢, 9月, (2023)．
  34. 鳥越大貴，田代一貴，増山直輝，能島裕介，伊藤諒適，三宅寿英，馬野元秀，応共鳴理論に基づく階層的トポロジカルクラスタリングにおけるクラスタリング性能向上方法の検討，第39回ファジィシステムシンポジウム講演論文集, 軽井沢, 9月, (2023)． 　
  35. 小西豪，増山直輝，能島裕介，アーカイブ個体群を用いた2段階ファジィ遺伝的機械学習の検討，第39回ファジィシステムシンポジウム講演論文集, 軽井沢, 9月, (2023)．

    36. 2022
  36. 木下貴登，増山直輝，能島裕介，石渕久生，Convergence-Diversity Diagramのためのパレート最適近似手法の検討，第16回進化計算シンポジウム2022講演論文集，pp. 185-192，札幌，12月（2022）
  37. 西川毅，増山直輝，能島裕介，石渕久生，適応共鳴理論に基づくクラスタリング手法によるマルチラベル識別器の改良，インテリジェント・システム・シンポジウム2022講演論文集，pp. 1-6，神戸，9月 (2022).
  38. 田代一貴，増山直輝，能島裕介，石渕久生，階層的トポロジカルクラスタリング手法における階層化方法の比較検討，第38回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2022)．
  39. 小西豪，増山直輝，能島裕介，石渕久生，精度に特化した最適化を最初に行う多目的ファジィ遺伝的機械学習，第38回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2022)．
  40. 中川夢斗，木下貴登，増山直輝，能島裕介，石渕久生，社会シミュレーションによる経済支援施策の進化型最適設計，第38回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2022)．
  41. 西浦弘樹，増山直輝，能島裕介，石渕久生，公平性を導入した多目的ファジィ遺伝的機械学習，第38回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2022)．
  42. 川野弘陽，Eric Vernon，増山直輝，能島裕介，石渕久生，２段階棄却オプションを導入したファジィ識別器の精度と識別拒否のトレードオフ解析，第38回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2022)．

    43. 2021
  43. 木下貴登，増山直輝，能島裕介，石渕久生，進化型多目的最適化アルゴリズムの分割的性能評価，第15回進化計算シンポジウム2021講演論文集，pp. 328-335，オンライン，12月 (2021).
  44. 面﨑祐一，増山直輝，能島裕介，石渕久生，マルチラベル多目的ファジィ遺伝的機械学習に対する進化型多目的マルチタスク最適化の適用，第15回進化計算シンポジウム2021講演論文集，pp. 240-247，オンライン，12月 (2021).
  45. 藤井祐人，増山直輝，能島裕介，石渕久生，2目的最適化問題変換に基づく進化型マルチモーダル多目的最適化アルゴリズムへの差分進化の適用，第15回進化計算シンポジウム2021講演論文集，pp. 156-162，オンライン，12月 (2021).
  46. 瓜田俊貴，花田泰生，増山直輝，能島裕介，石渕久生，実世界最適化問題への進化型多目的マルチタスク最適化手法の適用，第15回進化計算シンポジウム2021講演論文集，pp. 72-79，オンライン，12月 (2021).
  47. 面崎祐一，増山直輝，能島裕介，石渕久生，多目的ファジィ遺伝的機械学習におけるルール追加型ミシガン操作，インテリジェント・システム・シンポジウム2021講演論文集，オンライン，9月 (2021).
  48. 山田友菜，増山直輝，能島裕介，石渕久生，パラメータの自動設定機構を導入した階層的トポロジカルクラスタリング，インテリジェント・システム・シンポジウム2021講演論文集，オンライン，9月 (2021).
  49. 川野弘陽，Eric Vernon，増山直輝，能島裕介，石渕久生，複数の閾値を用いた棄却オプションの導入におけるファジィ識別器への影響調査，第37回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2021)．
  50. 瀧川弘毅，増山直輝，能島裕介，石渕久生，属性ごとに異なる形状のメンバシップ関数を用いたファジィ識別器設計，第37回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2021)．
  51. 吉永貴政，増山直輝，能島裕介，石渕久生，マルチラベル識別問題のための適応共鳴理論に基づくトポロジカルクラスタリング，第37回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2021)．
  52. 尼子就都，増山直輝，能島裕介，石渕久生，適応共鳴理論に基づくトポロジカルクラスタリングのための警戒パラメータの自動推定手法，第37回ファジィシステムシンポジウム講演論文集, オンライン, 9月, (2021)．
  53. 木下貴登，増山直輝，能島裕介，石渕久生，適応共鳴理論に基づくクラスタリングを用いた進化型多目的最適化アルゴリズム，第20回進化計算学会研究会講演論文集，オンライン，9月，(2021)．

    54. 2020
  54. 木下貴登，増山直輝，能島裕介，石渕久生，クラスタリング手法を用いた適応的分割に基づく進化型多目的最適化アルゴリズムの性能評価，第14回進化計算シンポジウム2020講演論文集，pp. 27-34，岐阜，12月 (2020)．
  55. 矢野真綾，増山直輝，能島裕介，石渕久生，制約付き多目的マルチモーダル距離最小化問題，第14回進化計算シンポジウム2020講演論文集，pp. 151-159，岐阜，12月 (2020)．
  56. 夏目和弥，増山直輝，能島裕介，石渕久生，複数データを用いた進化型多目的最適化による畳み込みニューラルネットワークのハイパーパラメータ最適化，ファジィシステムシンポジウム2020講演論文集， pp. 41-46，福岡，9月 (2020).
  57. 藤井祐人，増山直輝，能島裕介，石渕久生，2目的最適化問題への変換に基づく進化型マルチモーダル多目的最適化アルゴリズム，第36回ファジィシステムシンポジウム講演論文集，pp. 53-58，福岡，9月 (2020).
  58. 面崎祐一，増山直輝，能島裕介，石渕久生，マルチラベル識別問題におけるファジィ遺伝的機械学習の多目的最適化と多数目的最適化の比較，第36回ファジィシステムシンポジウム講演論文集，pp. 47-52，福岡，9月 (2020).
  59. 西原光洋，増山直輝，能島裕介，石渕久生，少数派クラスの識別性能を高めたMichigan型ファジィ遺伝的機械学習手法，第36回ファジィシステムシンポジウム講演論文集，pp. 367-372，福岡，9月 (2020)．
  60. 坪田一希，増山直輝，能島裕介，尼子就都，石渕久生，適応共鳴理論に基づいたトポロジカルクラスタリング手法による識別器設計，第36回ファジィシステムシンポジウム講演論文集，pp. 441-446，福岡，9月 (2020).

    61. 2019
  61. 橋本龍一，増山直輝，能島裕介，石渕久生，Multitask MOEA/D のタスク間交叉時における重みベクトルを用いた親個体選択による探索性能への影響調査，第13回進化計算シンポジウム2019講演論文集，pp. 246-253，兵庫，12月(2019)．
  62. 花田泰生，増山直輝，能島裕介，石渕久生，実問題に基づく制約付き多目的最適化問題の最適解集合に関する調査，第13回進化計算シンポジウム2019講演論文集，pp. 163-168，兵庫，12月(2019)．
  63. 西原光洋，増山直輝，能島裕介，石渕久生，少数派クラスの識別性能を高めたMichigan型ファジィ遺伝的機械学習手法，インテリジェント・システム・シンポジウム2019講演論文集，富山，9月 (2019).
  64. 深瀬貴史，増山直輝，能島裕介，石渕久生，2目的最適化問題への変換に基づく制約付き進化型多目的最適化手法，インテリジェント・システム・シンポジウム2019講演論文集，富山，9月 (2019).
  65. 尼子就都，増山直輝，能島裕介，石渕久生，クラスタリング手法における距離尺度の影響調査，第35回ファジィシステムシンポジウム講演論文集，pp. 121-126，大阪，8月 (2019)．
  66. 橋本龍一，増山直輝，能島裕介，石渕久生，進化型多目的マルチタスキングにおける他タスクの親個体の選択方法の違いによる探索性能への影響調査，第35回ファジィシステムシンポジウム講演論文集，pp. 59-64，大阪，8月 (2019)．
  67. 入江勇斗，増山直輝，能島裕介，石渕久生，クラス増分学習可能なファジィ遺伝的機械学習手法の提案，第35回ファジィシステムシンポジウム講演論文集，pp. 53-58，大阪，8月 (2019).
  68. 夏目和弥，増山直輝，能島裕介，石渕久生，遺伝的アルゴリズムによる畳み込みニューラルネットワークのハイパーパラメータ最適化，第35回ファジィシステムシンポジウム講演論文集，pp. 47-52，大阪，8月 (2019)．
  69. 面崎祐一，増山直輝，能島裕介，石渕久生，Fuzzy Markup Languageを用いたファジィシステムの開発，第35回ファジィシステムシンポジウム講演論文集，pp. 1-6，大阪，8月 (2019).

    70. 2018
  70. 荒張巧樹，増山直輝，能島裕介，石渕久生，マルチラベル分類に適応した多目的ファジィ遺伝的機械学習，第12回進化計算シンポジウム2018講演論文集，pp. 43-50，福岡，12月 (2018)
  71. 今田諒，増山直輝，能島裕介，石渕久生，IGDの参照点集合と選好される解分布の対応関係の調査，第12回進化計算シンポジウム2018講演論文集，pp. 99-106，福岡，12月 (2018)
  72. 橋本龍一，増山直輝，能島裕介，石渕久生，進化型多目的マルチタスキングにおけるタスク間の個体情報の伝達による探索性能への影響調査，第12回進化計算シンポジウム2018講演論文集，pp. 345-352，福岡，12月 (2018)
  73. 入江勇斗，増山直輝，能島裕介，石渕久生，クラス増加問題へのファジィ遺伝的機械学習の適用性の検討，第34 回ファジィシステムシンポジウム 講演論文集，pp. 578-583，愛知，9月 (2018)
  74. Y. Tanigaki, N. Masuyama, Y. Nojima, and H. Ishibuchi, “Approximation of the Pareto optimal solutions by a neural network based on solutions obtained by evolutionary algorithms,” Proc. of the 2018 JPNSEC International Workshop on Evolutionary Computation, 2 pages, Shenzhen, China, September 2018.
  75. R. Imada, Y. Tanigaki, N. Masuyama, Y. Nojima, and H. Ishibuchi, “Adaptation of weight vectors and the neighborhood size in MOEA/D for inverted triangular Pareto fronts,” Proc. of the 2018 JPNSEC International Workshop on Evolutionary Computation, 6 pages, Shenzhen, China, September 2018.
  76. R. Hashimoto, N. Masuyama, Y. Nojima, and H. Ishibuchi, “MOEA/D for multi-task multi-objective optimization,” Proc. of the 2018 JPNSEC International Workshop on Evolutionary Computation, ? pages, Shenzhen, China, September 2018.

    77. 2017
  77. 谷垣勇輝，能島裕介，石渕久生，複数の制約条件を持つ多目的最適化ベンチマーク問題作成のための基礎検討，進化計算シンポジウム2017講演論文集，pp. 250-257，北海道，12月 (2017).
  78. 深瀬貴史，能島裕介，増山直輝，石渕久生，MOEA/Dに対する制約条件取扱い手法の導入に関する検討，進化計算シンポジウム2017講演論文集，pp. 31-37，北海道，12月 (2017).
  79. 橋本龍一，能島裕介，増山直輝，石渕久生，複数車種の同時最適化問題に対する設計変数取扱い手法，進化計算シンポジウム2017講演論文集，pp. 502-509，北海道，12月 (2017).
  80. 今田諒，土井健，能島裕介，石渕久生，多様な多目的最適化問題に関するHypervolumeの選好調査，進化計算シンポジウム2017講演論文集，pp. 153-160，北海道，12月 (2017).
  81. 船越貴寛, 能島裕介, 石渕久生, 複数車種の同時最適化問題に対するMOEA/D-AOFの探索性能の調査, 第13回進化計算学会研究会講演集, pp. 49-56, 滋賀, 9月 (2017)
  82. 武村周治, 能島裕介, 石渕久生, 複数サーバを用いた並列分散型ファジィ遺伝的機械学習によるビッグデータ処理, 第13回進化計算学会研究会講演集, pp. 106-109, 滋賀, 9月 (2017)
  83. 今田諒, 能島裕介, 石渕久生, 多様なパレートフロントに対するHypervolume最適分布に関する調査, 第13回進化計算学会研究会講演集, pp. 11-19, 滋賀, 9月 (2017)
  84. 谷垣勇輝，明渡直哉，能島裕介，石渕久生，ハイパーヒューリスティックで得られた進化型多目的最適化アルゴリズムの汎用性の検証，第61回システム制御情報学会研究発表講演会 論文集，(CD-ROM, 6ページ)，京都，5月 (2017)

    85. 2016
  85. 能島裕介，谷垣勇輝，石渕久生，進化型多目的最適化により得られた解集合からの多目的知識獲得，第10回進化計算シンポジウム2016講演論文集，pp. 418-425，千葉，12月 (2016).
  86. 今田諒，能島裕介，石渕久生，異なる実装によるNSGA-IIIの探索性能への影響調査，第10回進化計算シンポジウム2016講演論文集，pp. 21-28，千葉，12月 (2016).
  87. 谷垣勇輝，能島裕介，石渕久生，探索空間に注目した進化型多目的最適化手法の探索性能比較，第10回進化計算シンポジウム2016講演論文集，pp. 37-44，千葉，12月 (2016).
  88. 武村周治，能島裕介，石渕久生，多目的ファジィ遺伝的機械学習における並列分散実装の過学習に対する効果，第10回進化計算シンポジウム2016講演論文集，pp. 123-130，千葉，12月 (2016).
  89. 伊藤彰悟，谷垣勇輝，能島裕介，石渕久生，MOEA/Dの交叉操作の切り替えによる性能への影響調査，第10回進化計算シンポジウム2016講演論文集，pp. 410-417，千葉，12月 (2016).
  90. 土井健，今田諒，能島裕介，石渕久生，分割に基づく進化型多目的最適化アルゴリズムにおける参照点生成の影響調査，第10回進化計算シンポジウム2016講演論文集，pp. 468-475，千葉，12月 (2016).
  91. 能島裕介, 石渕久生, 識別拒否を考慮した多目的ファジィ識別器設計, 第26回インテリジェント・システム・シンポジウム講演論文集, pp. 82-87, 大阪, 10月 (2016).
  92. 今田諒, 瀬戸口悠, 能島裕介, 石渕久生, 異なる参照点分布を用いたNSGA-IIIの探索性能の調査, 第11回進化計算学会研究会資料集, pp. 65-72, 神戸, 9月 (2016).
  93. 荒張巧樹, 武村周治, 能島裕介, 石渕久生, 多目的ファジィ遺伝的機械学習に特化したスカラー関数の提案, 第11回進化計算学会研究会資料集, pp. 190-196, 神戸, 9月 (2016).
  94. 瀬戸口悠, 今田諒, 能島裕介, 石渕久生, 参照点の位置とパレートフロントの形状がHypervolumeを最大化する解分布に与える影響, 第11回進化計算学会研究会資料集, pp. 163-182, 神戸, 9月 (2016).
  95. 土井健, 能島裕介, 石渕久生, 二種類の重みベクトルを用いた汎用型MOEA/Dの提案, 第11回進化計算学会研究会資料集, pp. 197-214, 神戸, 9月 (2016).
  96. 谷垣勇輝, 能島裕介, 石渕久生, MOEA/Dにおける近傍選択手法が探索に及ぼす影響, 第60回システム制御情報学会研究発表講演会講演論文集, (CD-ROM, 6ページ), 京都, 5月 (2016).
  97. 米山裕乃, 船越貴寛, 能島裕介, 石渕久生, NSGA-IIにおける決定変数と目的関 数の離散化による影響, 第10回進化計算学会研究会資料集, pp. 200-209, 神奈川, 3月 (2016)
  98. 船越貴寛, 能島裕介, 石渕久生, 擬似乱数の実装方法の違いによるPolynomial Mutationへの影響, 第10回進化計算シンポジウム講演論文集, pp.165-172, 神奈川, 3月 (2016)
  99. 土井健, 能島裕介, 石渕久生, MOEA/DのPBIを拡張したスカラー化関数の提案, 第10回進化計算学会研究会資料集, pp. 72-87, 神奈川, 3月 (2016)
  100. 今田諒, 瀬戸口悠, 能島裕介, 石渕久生, DTLZ最大化問題とWFG最大化問題を用いたNSGA-IIIの探索性能の調査, 第10回進化計算学会研究会資料集, pp. 62-71, 神奈川, 3月（2016年）

    101. 2015
  101. 土井健, 能島裕介, 石渕久生, MOEA/Dにおける重みベクトルの分布が解の分布に与える影響, 第9回進化計算シンポジウム講演論文集, pp. 1-8, 愛知, 12月 (2015)
  102. 船越貴寛, 能島裕介, 石渕久生, チェビシェフ関数を用いたMOEA/Dにおける実装方法の違いによる探索性能への影響, 第9回進化計算シンポジウム講演論文集, pp. 9-16, 愛知, 12月 (2015)
  103. 谷垣勇輝, 能島裕介, 石渕久生, 進化型多目的最適化アルゴリズム評価のためのメタ最適化を用いた問題生成, 第9回進化計算シンポジウム講演論文集, pp. 17-24, 愛知, 12月 (2015)
  104. 武村周治, 能島裕介, 石渕久生, 多目的ファジィ遺伝的機械学習におけるアルゴリズムの違いによる探索性能への影響, 第9回進化計算シンポジウム講演論文集, pp. 25-32, 愛知, 12月 (2015)
  105. 増田広行, 能島裕介, 石渕久生, パレートフロントの形状を指定した多目的最適化テスト問題, 第9回進化計算シンポジウム講演論文集, pp. 33-41, 愛知, 12月 (2015)
  106. 須藤尭彦, 後藤和志, 能島裕介, 石渕久生, 繰り返し囚人のジレンマゲームにおける7bit戦略の不思議な挙動に関する追加調査, 第9回進化計算シンポジウム講演論文集, pp. 41-48, 愛知, 12月 (2015)
  107. 後藤和志，須藤尭彦，能島裕介，石渕久生，繰り返し囚人のジレンマゲームにおける自身の行動のみを記憶する戦略を用いたアンサンブルによる協調行動への進化と影響の調査，第9回進化計算学会研究会資料集，pp. 24-30，神戸，9月 (2015)
  108. 須藤尭彦，後藤和志，能島裕介，石渕久生，同一空間構造内で異なる記憶内容を持つ戦略表現を用いた繰り返し囚人のジレンマゲームにおける協調行動の進化，第9回進化計算学会研究会資料集，pp. 37-48，神戸，9月 (2015)
  109. 船越貴寛，瀬戸口悠，能島裕介，石渕久生，NSGA-IIおよびMOEA/Dにおける実装方法の違いによる影響の調査，第9回進化計算学会研究会資料集，pp. 49-64，神戸，9月 (2015)
  110. 増田広行，能島裕介，石渕久生，進化型多目的最適化アルゴリズムの外部個体群を用いた性能評価，第9回進化計算学会研究会資料集，pp. 76-85，神戸，9月 (2015)
  111. 土井健，能島裕介，石渕久生，MOEA/Dのスカラー化関数の選択が重みベクトルと解の関係に与える影響，第9回進化計算学会研究会資料集，pp. 109-124, 神戸，9月 (2015)
  112. 谷垣優輝，能島裕介，石渕久生，探索時期に合わせた進化型多目的最適化アルゴリズムの選択，第9回進化計算学会研究会資料集，pp. 131-137，神戸，9月 (2015)
  113. 高橋佑治，能島裕介，石渕久生，並列分散型多目的ファジィ遺伝的機械学習における目的関数の回転，第31回ファジィシステムシンポジウム予稿集，pp. 675-680，東京，9月 (2015)
  114. 渡邊一弘，能島裕介，石渕久生，ミシガン型ファジィ遺伝的機械学習における異なるパターン選択方法を用いた複数パターンからの新規ルール生成，第31回ファジィシステムシンポジウム予稿集，pp. 687-692，東京，9月 (2015)
  115. 能島裕介，石渕久生，個人情報保護を考慮した並列分散型ファジィ遺伝的機械学習，電気学会C部門研究会知覚情報研究会，(4 pages), 東京，4月（2015）

    116. 2014
  116. 瀬戸口悠†，谷垣勇輝，増田広行，能島裕介，石渕久生，Knee point探索を行う進化型多目的最適化アルゴリズムKR-NSGA-IIの改良，第８回進化計算シンポジウム2014講演論文集，pp. 391-398，広島，12月（2014）
  117. 増田広行†，谷垣勇輝，瀬戸口悠，能島裕介，石渕久生，進化型多目的最適化アルゴリズムにおける解の優越関係を考慮した性能評価指標，第８回進化計算シンポジウム2014講演論文集，pp. 351-357，広島，12月（2014）
  118. 後藤和志，須藤尭彦，能島裕介，石渕久生，繰り返し囚人のジレンマゲームにおける7bit戦略の不思議な挙動の解析，第８回進化計算シンポジウム2014講演論文集，pp. 282-287，広島，12月（2014）
  119. 須藤尭彦†，後藤和志，†能島裕介，石渕久生，記憶内容の異なる戦略によるアンサンブル行動選択が協調行動の進化に与える影響，第８回進化計算シンポジウム2014講演論文集，pp. 169-175，広島，12月（2014）
  120. 谷垣勇輝，増田広行，瀬戸口悠，能島裕介，石渕久生，多目的遺伝的局所探索における探索構造の最適化，第８回進化計算シンポジウム2014講演論文集，pp. 65-71，広島，12月（2014）
  121. 増田広行，能島裕介，石渕久生，縮退したパレートフロントを持つ多目的最適化テスト問題における解の最適性について，第８回進化計算シンポジウム2014講演論文集，pp. 1-4，広島，12月（2014）
  122. 高橋佑治，能島裕介，石渕久生，多目的ファジィ遺伝的機械学習の並列分散実装，第6回コンピューテーショナル・インテリジェンス研究会資料集，pp. 20-23，大阪，12月 (2014)
  123. 渡邊一弘，能島裕介，石渕久生，ファジィ遺伝的機械学習における複数パターンを用いた新規ルール生成，第6回コンピューテーショナル・インテリジェンス研究会資料集，pp. 16-19，大阪，12月 (2014)
  124. 高橋佑治, 能島裕介, 石渕久生, 不均等ファジィ集合を用いたファジィ識別器設計, 第30回ファジィシステムシンポジウム予稿集, pp. 196-199, 高知, 9月 (2014)
  125. 谷垣勇輝, 能島裕介, 石渕久生, 多目的最適化問題における問題の性質と進化型多目的最適化アルゴリズムの探索性能の関係, 第30回ファジィシステムシンポジウム予稿集, pp. 416-419, 高知, 9月 (2014)
  126. 増田広行, 谷垣勇輝, 能島裕介, 石渕久生, 進化型多目的最適化アルゴリズムの性能評価指標における参照解の影響調査, 第7回進化計算学会研究会資料集, pp. 48-52, 大阪, 8月 (2014)
  127. 谷垣勇輝，能島裕介，石渕久生，探索方法の選択による多目的遺伝的局所探索のアルゴリズム最適化，第7回進化計算学会研究会資料集，pp. 42-47, 大阪, 8月 (2014)
  128. 上羽浩二，須藤尭彦，能島裕介，石渕久生，(1+1)ES型対話型進化計算における再提示操作の探索速度への影響，第7回進化計算学会研究会資料集, pp. 131-133, 大阪, 8月 (2014)
  129. 後藤和志，須藤尭彦，能島裕介，石渕久生，繰り返し囚人のジレンマゲームにおける戦略進化過程のGUIによる分析，第7回進化計算学会研究会資料集，pp. 134-137，大阪，8月　(2014)
  130. 須藤尭彦, 上羽浩二, 能島裕介, 石渕久生, 対話型進化計算における効率的な解生成方法の探索, 第7回進化計算学会研究会資料集, pp. 127-130, 大阪, 8月 (2014)
  131. 増田広行，谷垣勇輝，能島裕介，石渕久生，近傍交叉を用いた進化型多目的最適化手法の多数目的ナップサック問題への適用，第58回システム制御情報学会研究発表講演会講演論文集，CD-ROM 4ページ，京都，5月 (2014)
  132. 須藤尭彦，能島裕介，石渕久生，繰り返し囚人のジレンマゲームにおけるプレイヤーの記憶内容の違いか協調戦略の進化に与える影響，第58回システム制御情報学会研究発表講演会講演論文集，CR-ROM 6ページ，京都，5月 (2014)
  133. 須藤尭彦, 能島裕介, 石渕久生, アンサンブル行動選択を用いた繰り返し囚人のジレンマゲームにおける協調行動の進化, 第6回進化計算学会研究会資料集, pp. 88-89, 東京, 3月 (2014)
  134. 瀬戸口悠, 能島裕介, 石渕久生, 解の支配領域制御法に対する複数の参照点を用いたHypervolumeによる性能評価, 第6回進化計算学会研究会資料集, pp. 96-98, 東京, 3月 (2014)
  135. 須藤尭彦, 能島裕介, 石渕久生, (μ+1)対話型進化計算アルゴリズムにおける候補解の個数と評価回数の変化に対する個体生成方法の影響調査, 第6回進化計算学会研究会資料集, pp. 117-118, 東京, 3月 (2014)
  136. 上羽浩二, 須藤尭彦, 能島裕介, 石渕久生, (1+1)ES型対話型進化計算モデルへの1/5規則の適用, 第6回進化計算学会研究会資料集, pp. 152-155, 東京, 3月 (2014)
  137. 増田広行, 能島裕介, 石渕久生, 複数の参照点に対するHypervolume最大化を用いた多目的解集合最適化, 第6回進化計算学会研究会資料集, pp. 169-172, 東京, 3月 (2014)

    138. 2013
  138. 山根優和，能島裕介，石渕久生，並列分散型ファジィ遺伝的機械学習におけるファジィルールの結論部の更新， 第29回ファジィシステムシンポジウム予稿集，pp. 691-694, 大阪，9月 (2013)
  139. Ivarsson Patrik, 能島裕介, 石渕久生, GAssist の並列分散実装の効果に関する一考察, 第29回ファジィシステムシンポジウム予稿集, pp. 413-418 , 大阪, 9月 (2013)
  140. 高橋佑治, 山根優和, 能島裕介, 石渕久生, ファジィ遺伝的機械学習のMichigan操作における削除ルール選択, 第29回ファジィシステムシンポジウム予稿集, pp. 687-690 , 大阪, 9月 (2013)
  141. 能島裕介，石渕久生，ファジィ関係性ルールを用いた多目的知識獲得，第57回システム制御情報学会研究発表講演会講演論文集，CR-ROM 4ページ，神戸，5月 (2013)
  142. 須藤尭彦，能島裕介，石渕久生，繰り返し囚人のジレンマゲームにおける協調行動の進化へのエージェント数と対戦人数の影響，第57回システム制御情報学会研究発表講演会講演論文集，CR-ROM 6ページ，神戸，5月 (2013)
  143. 谷垣勇輝，明渡直哉，山根優和，能島裕介，石渕久生，進化型多目的最適化における局所探索の重みベクトルの調査，第57回システム制御情報学会研究発表講演会講演論文集，CR-ROM 6ページ，神戸，5月 (2013)
  144. 須藤尭彦，星野洸一郎，能島裕介，石渕久生，ネットワーク上での繰り返し囚人のジレンマゲームにおける戦略進化， 第4回進化計算学会研究会資料集，pp. 36-39, 神奈川，3月 (2013)
  145. 谷垣勇輝，明渡直哉，山根優和，能島裕介，石渕久生，進化型多目的最適化の局所探索時における動的重みベクトルの適応， 第4回進化計算学会研究会資料集，pp. 79-83, 神奈川，3月 (2013)

    146. 2012
  146. 明渡直哉，能島裕介，石渕久生，Hyper-Heuristicsを用いた多目的遺伝的局所探索の最適なパラメータの決定，進化計算シンポジウム2012講演論文集，pp. 468-473，長野，12月（2012）
  147. 星野洸一郎，能島裕介，石渕久生，意思決定者が複数の解を同時に評価できない状況での単純な対話型進化計算アルゴリズムの一実装， 進化計算シンポジウム2012講演論文集，pp. 175-180，長野，12月（2012）
  148. 星野洸一郎，能島裕介，石渕久生，意思決定者が複数の解を同時に評価できない状況での対話型進化計算の定式化， 第3回進化計算学会研究会資料集，pp. 113-114，広島，9月 (2012)
  149. 山根優和，上田彬人，田所直，能島裕介，石渕久生，遺伝的ファジィルール選択における適応度関数の修正，第28回ファジィシステムシンポジウム予稿集，pp. 723-726, 名古屋，9月（2012）
  150. 山根優和，能島裕介，石渕久生，並列分散型ファジィ遺伝的機械学習によるアンサンブル識別器の設計，第28回ファジィシステムシンポジウム予稿集，pp. 715-718, 名古屋，9月（2012）
  151. 山根優和，能島裕介，石渕久生，進化型多目的最適化における目的関数への乱数付加の影響，第56回システム制御情報学会研究発表講演会講演論文集，pp. 427-428，京都，5月 (2012)
  152. 星野洸一郎，能島裕介，石渕久生，繰り返し囚人のジレンマゲームにおけるユークリッド距離に基づく近傍決定モデル，第2回進化計算学会研究会／第8回進化計算フロンティア研究会合同研究会資料集，pp. 76-81，大阪，3月 (2012)
  153. 武内孝頼，明渡直哉，能島裕介，石渕久生，決定変数空間における解集合の多様性維持を考慮した進化型多目的最適化，第2回進化計算学会研究会／第8回進化計算フロンティア研究会合同研究会資料集，pp. 147-150，大阪，3月 (2012)

    154. 2011
  154. 三原新吾，能島裕介，石渕久生：並列分散型遺伝的機械学習における環境の変化が及ぼす汎化性能への影響，進化計算シンポジウム2011講演論文集，pp. 203-208，宮城，12月（2011）
  155. 明渡直哉，能島裕介，石渕久生：MOEA/Dの異なる近傍サイズによる決定変数空間における解集合の多様性への影響，進化計算シンポジウム2011講演論文集，pp. 267-272，宮城，12月（2011）
  156. 星野洸一郎，能島裕介，石渕久生：繰り返し囚人のジレンマゲームにおける対戦相手選択の違いによる協調進化への影響，進化計算シンポジウム2011講演論文集，pp. 153-158，宮城，12月（2011）
  157. 髙橋慶祐，星野洸一郎，前田淳兵，能島裕介，石渕久生：繰り返し囚人のジレンマゲームにおける異なる戦略表現の配置の影響を分析するための可視化インタフェース，第27回ファジィシステムシンポジウム講演論文集，pp. 965-968, 福井, 9月（2011）
  158. 明渡直哉，能島裕介，石渕久生：目的関数間の関連度合いの違いによる進化型多目的最適化アルゴリズムの探索性能への影響，第21回インテリジェント・システム・シンポジウム講演論文集，(CD-ROM)，神戸，9月（2011）
  159. 三原新吾，能島裕介，石渕久生：並列分散型ファジィ遺伝的機械学習の探索性能に対する個体移住操作の影響，第21回インテリジェント・システム・シンポジウム講演論文集，（CD-ROM）神戸，9月（2011）
  160. 一柳徳宏，大柳浩之，能島裕介，石渕久生：多目的最適化問題におけるHypervolume近似手法の改良，第55回システム制御情報学会研究発表講演会講演論文集，pp. 421-422，大阪，5月 (2011).
  161. 高橋慶祐，能島裕介，石渕久生：空間型繰り返し囚人のジレンマゲームにおける異なる戦略表現の配置について，第6回進化計算フロンティア研究会講演論文集，pp. 128-133，名古屋，3月 (2011).
  162. 田所直，山根優和，能島裕介，石渕久生：遺伝的ファジィルール選択へのルール削除及び追加操作の導入，第6回進化計算フロンティア研究会講演論文集，pp. 36-37，名古屋，3月（2011）．
  163. 明渡直哉，石渕久生，能島裕介：パレート最適立地探索問題への進化型多目的最適化アルゴリズムの適用，平成22年度(社)日本経営工学会関西支部 卒業論文・修士論文発表会講演予稿集，pp. 13-16，兵庫，3月（2011）．

    164. 2010
  164. 能島裕介，三原新吾，石渕久生：並列分散遺伝的ルール選択における学習用データの細分化，進化計算シンポジウム2010講演論文集，pp. 234-239，福岡，12月（2010）．
  165. 明渡直哉，能島裕介，石渕久生：パレート最適立地探索問題の提案とEMOアルゴリズムの適用，進化計算シンポジウム2010講演論文集，pp. 171-174，福岡，12月（2010）．
  166. 能島裕介，石渕久生：進化型多目的最適化によるファジィ識別器設計におけるメンバーシップ関数の調整，第20回インテリジェントシステムシンポジウム講演論文集，(USB, 2 pages)，東京，9月（2010）．ベストプレゼンテーション賞（和田賞）
  167. 一柳徳宏，明渡直哉，能島裕介，石渕久生：視覚的にEMOアルゴリズムの探索挙動を比較できる多数目的最適化問題の提案，第20回インテリジェントシステムシンポジウム講演論文集，(USB, 4 pages)，東京，9月（2010）．
  168. 三原新吾，能島裕介，石渕久生：並列分散型遺伝的ファジィルール選択によるアンサンブル識別器の設計，第26回ファジィシステムシンポジウム講演論文集，pp. 145-148，広島，9月（2010）．
  169. 三原新吾，能島裕介，石渕久生：並列分散型遺伝的ファジィルール選択における部分データ集合の交換操作が及ぼす部分個体群の進化への影響，第26回ファジィシステムシンポジウム講演論文集，pp. 482-487，広島，9月（2010）．
  170. 能島裕介，石渕久生：ユーザーの選好を目的関数とした進化型多目的最適化による対話型ファジィデータマイニング，第26回ファジィシステムシンポジウム講演論文集，pp. 159-162，広島，9月（2010）．
  171. 星野洸一郎，能島裕介，石渕久生，直前に提示された解との相対評価だけに基づく多目的進化計算，第3回進化計算フロンティア研究会予稿集，pp. 134-139，岡山，3月 (2010).

    172. 2009
  172. 能島裕介，石渕久生：ユーザーの選好を目的関数として用いる多目的遺伝的ファジィ知識獲得, 進化計算シンポジウム2009講演論文集, 沖縄, pp. 85-90, 12月 (2009).
  173. 一柳徳宏, 若松良彦, 能島裕介, 石渕久生：局所探索適用個体の選択方法と多目的遺伝的局所探索の性能との関係, 進化計算シンポジウム2009講演論文集, 沖縄, pp. 112-116, 12月 (2009).
  174. 一柳徳宏，若松良彦，能島裕介，石渕久生：効果的な局所探索を行う多目的Memeticアルゴリズムの提案，計測自動制御学会 システム・情報部門 学術講演会 2009 講演論文集，pp. 93-96，横浜，11月 (2009)．
  175. 一柳徳宏，若松良彦，能島裕介，石渕久生：多数目的最適化問題における局所探索の影響調査，計測自動制御学会 システム・情報部門 学術講演会 2009 講演論文集，pp. 125-130，横浜，11月 (2009)．
  176. 能島裕介，石渕久生：並列分散型遺伝的ファジィルール選択におけるデータ分割 の影響，第25回ファジィシステムシンポジウム講演論文集(CD-ROM)，筑波, 7月 (2009).
  177. 大柳浩之，能島裕介，石渕久生：空間構造内で異なる戦略表現形式を用いた場合 の繰り返し囚人のジレンマゲームにおける協調行動の進化，第25回ファジィシス テムシンポジウム 講演論文集(CD-ROM)，筑波, 7月 (2009).
  178. 中島悠介，能島裕介，石渕久生：多目的ファジィ遺伝的機械学習におけるNSGA- IIとMOEA/Dの探索能力の調査，第25回ファジィシステムシンポジウム 講演論文 集(CD-ROM)，筑波, 7月 (2009).
  179. 塚本実孝，坂根悠治，能島裕介，石渕久生，多数目的最適化のためのスカラー化関数を用いたHypervolumeの近似手法の提案，第53回システム制御情報学会研究発表講演会講演論文集，pp. 219-220，神戸，5月 (2009).
  180. 坂根悠治，塚本実孝，能島裕介，石渕久生，大規模な格子空間を持つ進化型多目的セルラーアルゴリズムの挙動，第53回システム制御情報学会研究発表講演会講演論文集，pp. 217-218，神戸，5月 (2009).
  181. 坂根悠治，塚本実孝，能島裕介，石渕久生，進化型多目的最適化アルゴリズムにおけるスカラー化適応度関数の適宜な使用，第53回システム制御情報学会研究発表講演会講演論文集，pp. 145-146，神戸，5月 (2009).
  182. 辻本裕基，一柳徳宏，能島裕介，石渕久生，単一目的最適解を進化型多目的最適化アルゴリズムの初期個体群に含める効果，第53回システム制御情報学会研究発表講演会講演論文集，pp. 143-144，神戸，5月 (2009).

    183. 2008
  183. 槇岡良祐，能島裕介，石渕久生，遺伝的アルゴリズムを用いた呼び割当最適化によるマルチカーエレベータ制御手法の提案，コンカレント工学研究会2008発表審査会予稿集，pp. 3-4, 大阪, 11月 (2008).
  184. 一柳徳宏, 能島裕介, 石渕久生，特定の目的に有効な局所探索操作を持つ多目的Memeticアルゴリズム，第18回インテリジェント・システム・シンポジウム講演論文集，pp. 369-372, 広島, 10月 (2008).
  185. 坂根悠治，塚本実孝，能島裕介，石渕久生，多数目的最適化のためのMOEA/D の性能に対する重みベクトル設定の影響，第18回インテリジェント・システム・シンポジウム講演論文集，pp. 377-380, 広島, 10月 (2008).
  186. 槇岡良祐，能島裕介，石渕久生，遺伝的アルゴリズムによるマルチカーエレベータのホームポジション最適化の効果，第18回インテリジェント・システム・シンポジウム講演論文集，pp. 215-218, 広島, 10月 (2008).
  187. 甲斐荘裕, 能島裕介, 石渕久生，ファジィ識別器における識別メカニズムの視覚インターフェースの開発，第24回ファジィシステムシンポジウム講演論文集, pp. 943-946，大阪, 9月 (2008).
  188. 桑島功，能島裕介，石渕久生，進化型多目的最適化によるパレート最適ファジィルールマイニング手法の提案，第24回ファジィシステムシンポジウム講演論文集, pp. 662-665，大阪, 9月 (2008).
  189. 坂根悠治，塚本実孝，能島裕介，石渕久生，進化型多数目的最適化におけるスカラー適応度関数の利用の効果，第24回ファジィシステムシンポジウム講演論文集, pp. 548-551，大阪, 9月 (2008).
  190. 塚本実孝，能島裕介，石渕久生，多数目的最適化のための単一目的GAと多目的GAのハイブリッド化，第52回システム制御情報学会研究発表講演会講演論文集，pp. 257-258，京都，5月 (2008).
  191. 一柳徳宏, 能島裕介, 石渕久生，多目的Memeticアルゴリズムにおける局所探索確率の影響，第52回システム制御情報学会研究発表講演会講演論文集，pp. 255-256，京都，5月 (2008).
  192. 槇岡良祐，北野夢佳，能島裕介，石渕久生，遺伝的アルゴリズムを用いたマルチカーエレベータにおけるホームポジションの最適化，コンカレント工学研究会2007講演論文集，pp. 17-22, 徳島，1月 (2008).

    193. 2007
  193. 能島裕介，石渕久生，並列分散型遺伝的知識獲得，進化計算シンポジウム2007講演論文集, pp. 103-106，北海道，12月 (2007)．
  194. 石渕久生，塚本実孝，能島裕介，進化型多数目的最適化，進化計算シンポジウム2007講演論文集, pp. 47-50，北海道，12月 (2007)．
  195. 桑島功，能島裕介，石渕久生，非対称台形型ファジィ集合を用いた多目的データマイニング，平成19年電気学会電子・情報・システム部門大会講演論文集, pp. 725-728，堺，９月 (2007)．
  196. 能島裕介，桑島功，石渕久生，並列分散型遺伝的ファジィルール選択の性能評価，平成19年電気学会電子・情報・システム部門大会講演論文集, pp. 1241-1244，堺，９月 (2007)．
  197. 塚本実孝，一柳徳宏，能島裕介，石渕久生，進化型多目的最適化における類似性に基づく親個体選択手法の改良，平成19年電気学会電子・情報・システム部門大会講演論文集, pp. 1169-1172，堺，９月 (2007)．
  198. 塚本実孝，石渕久生，能島裕介，Hypervolumeに基づく繰り返し型EMOアプローチの改良，第23回ファジィシステムシンポジウム講演論文集, pp. 593-596，名古屋，８月 (2007)．
  199. 能島裕介，石渕久生，アンサンブル識別器設計のための遺伝的ルール選択のコード化，第23回ファジィシステムシンポジウム講演論文集, pp. 589-592，名古屋，８月 (2007)．奨励賞
  200. 桑島功，能島裕介，石渕久生，ファジィ識別器における精度と複雑性のトレードオフ解析，第23回ファジィシステムシンポジウム講演論文集, pp. 625-628，名古屋，８月 (2007)．
  201. 大原健，能島裕介，石渕久生，交通渋滞解消のための車車間通信を用いた道路情報共有の有効性，第23回ファジィシステムシンポジウム講演論文集, pp. 771-774，名古屋，８月 (2007)．
  202. 一柳徳宏，能島裕介，石渕久生，問題領域の知識を利用した局所探索を持つ多目的Memeticアルゴリズム，第23回ファジィシステムシンポジウム講演論文集, pp. 62-65，名古屋，８月 (2007)．
  203. 桑島功，能島裕介，石渕久生，進化型多目的最適化による識別器の設計，第17回インテリジェント・システム・シンポジウム講演論文集, pp. 151-154，名古屋，８月 (2007)．
  204. 塚本実孝，石渕久生，能島裕介，進化型多目的最適化における多様性維持のための交叉操作の提案，第17回インテリジェント・システム・シンポジウム講演論文集, pp. 321-324，名古屋，８月 (2007)．
  205. 塚本実孝，大原健，石渕久生，能島裕介，バイナリコード型GAにおける個体群の多様性を維持する交叉操作，第51回システム制御情報学会研究発表講演会講演論文集, pp. 125-126，京都，５月 (2007)．
  206. 北野夢佳，大原健，石渕久生，能島裕介，２種類の近傍構造を用いたセルラーGA，第51回システム制御情報学会研究発表講演会講演論文集, pp. 147-148，京都，５月 (2007)．
  207. 塚本実孝，石渕久生，能島裕介，Hypervolumeに基づいた繰り返し型EMOアプローチの提案，第51回システム制御情報学会研究発表講演会講演論文集, pp. 115-116，京都，５月 (2007)．
  208. 桑島功，能島裕介，石渕久生，遺伝的ルール選択における候補ルールの条件緩和，第51回システム制御情報学会研究発表講演会講演論文集, pp. 145-146，京都，５月 (2007)．
  209. 大原健，能島裕介，石渕久生，車車間通信を用いた道路情報の共有，第17回ソフトサイエンス・ワークショップ講演論文集, pp. 152-153，守口，３月 (2007)．
  210. 桑島功，能島裕介，石渕久生，ε-パレート最適ルールを用いた遺伝的ファジィルール選択，第17回ソフトサイエンス・ワークショップ講演論文集, pp. 202-205，守口，３月 (2007)．
    2006
  211. 能島裕介，石渕久生，進化型多目的最適化によるアンサンブル識別器の設計，第49回自動制御連合講演会，CD-ROM，2 pages，神戸，11月 (2006)．
  212. 大原健，能島裕介，石渕久生，交通渋滞解消のための中央管理型及び分散管理型経路選択手法の性能比較，第22回ファジィシステムシンポジウム講演論文集, pp. 909-912, 札幌，9月 (2006)．
  213. 桑島功，能島裕介，石渕久生，データ分割に基づく遺伝的ルール選択の大規模データへの適用，第22回ファジィシステムシンポジウム講演論文集, pp. 101-102, 札幌，9月 (2006)
  214. 桑島功，能島裕介，石渕久生，データマイニングの後処理としての遺伝的ルール選択の有効性，第50回システム制御情報学会研究発表講演会講演論文集, pp. 19-20, 京都，5月 (2006)
  215. 土井努，鳴川要，能島裕介，石渕久生，多目的GAの１回実行と単一目的GAの複数回実行との比較，第50回システム制御情報学会研究発表講演会講演論文集，pp. 11-12，京都，5月 (2006)．

    216. 2005
  216. 能島裕介，桑島功，石渕久生，パターン識別問題におけるルール抽出と進化型多目的ルール選択，第50回システム制御情報学会研究発表講演会講演論文集，pp. 47-48，京都，5月 (2006)
  217. 鳴川要, 能島裕介, 石渕久生，ファジィルールに基づく識別システムの多目的設計に対するNSGA-IIアルゴリズムの改良，第21回ファジィシステムシンポジウム講演論文集，pp. 175-178，東京，９月 (2005).
  218. 能島裕介，鳴川要，石渕久生，進化型多目的最適化における重複解の取り扱い，第49回システム制御情報学会研究発表講演会講演論文集，pp. 285-286，京都，5月 (2005)

    219. 2004
  219. 能島裕介，小島史男，久保田直行，パートナーロボットのためのジェスチャ認識と進化的軌道生成，第14回インテリジェント・システム・シンポジウム講演論文集，pp. 7-10，高知，10月 (2004)　ベストプレゼンテーション賞受賞
  220. 能島裕介，小島史男，久保田直行，パートナーロボットのための手渡し行動の生成と蓄積，第2回KES国際会議日本支部講演会論文集，pp. 103-106，大阪，9月 (2004)

    221. 2003
  221. 能島裕介，小島史男，久保田直行，パートナーロボットのための対話型動作計画と多目的行動調停，第19回ファジィシステムシンポジウム講演論文集，pp. 95-98，(2003)
  222. 能島裕介，小島史男，久保田直行，多目的行動調停に基づく移動ロボットの局所エピソード記憶による動的環境適応，第47回システム制御情報学会研究発表講演会講演論文集，pp. 467-470，(2003)

    223. 2002
  223. 能島裕介，小島史男，久保田直行，多目的行動調停則の獲得に基づく移動ロボットの状況と行動，第11回日本ファジィ学会北信越支部シンポジウム，CDROM，(2002)
  224. 北川幸宏，能島裕介，小島史男，久保田直行，多目的行動調停と環境予測に基づく移動ロボットの動的環境下における行動制御，第46回システム制御情報学会研究発表講演会講演論文集，pp. 547-548, (2002)

    225. 2001
  225. 能島裕介，予期による知覚と行動多様性，The Proceedings of Be Ambitious Conference 2001, CDROM, (2001)
  226. 能島裕介，小島史男，久保田直行，知能ロボットのための情動モデルによる割り込み機能，第11回インテリジェント・システム・シンポジウム講演論文集，pp. 189-192, (2001)
  227. 能島裕介，小島史男，久保田直行，情動モデルに基づくペットロボットの制御と学習，第45回システム制御情報学会研究発表講演会講演論文集，pp. 257-258, (2001)

    228. 2000
  228. 能島裕介，久保田直行，小島史男，移動ロボットにおける直接知覚と行動，日本ファジィ学会ファジィ・コンピューティング研究部会ワークショップ「第11回言いたい放題の合宿研究会」，(2000)
  229. 能島裕介，久保田直行，小島史男，構造化知能における知覚系と行動系の相互学習機構，第44回システム制御情報学会研究発表講演会，pp. 183-184，(2000)
  230. 能島裕介，（久保田直行，小島史男），知能ロボットに基づくデュアルラーニング，Be Ambitious Conference 2000, pp. 75-76, (2000)

    231. 1999
  231. 能島裕介，久保田直行，小島史男，福田敏男，構造化知能を持つ移動ロボットの行動獲得，第9回インテリジェント・システム・シンポジウム，pp. 780-783，(1999)
  232. 久保田直行，能島裕介，小島史男，福田敏男，構造化知能を持つロボットシステムの行動制御の次元について，ポトラックシンポジウム'99，pp. 291-292，(1999)
  233. 久保田直行，能島裕介，小島史男，福田敏男，構造化知能を持つ移動ロボットのルール抽出，第5回創発システムシンポジウム，(1999)
  234. 久保田直行，能島裕介，小島史男，福田敏男，ファジィ生産スケジューリングのための遺伝的アルゴリズム，ロボティクス・メカトロニクス講演会'99，CD-ROM Proc.，(1999)
  235. 能島裕介，久保田直行，小島史男，福田敏男，柴田晋，稲田雄二，山本栄市，自在搬送システムの経路計画および制御，ロボティクス・メカトロニクス講演会'99，CD-ROM Proc.，(1999)

    236. 1998
  236. 久保田直行，小島史男，能島裕介，福田敏男，遺伝的アルゴリズムと局所意思決定を用いた知的搬送システム，第8回インテリジェント・システム・シンポジウム，pp. 433-436，(1998)
  237. 久保田直行，小島史男，能島裕介，福田敏男，遺伝的アルゴリズムによる知的搬送システムの最適化，第７回日本ファジィ学会北信越支部ファジィシンポジウム，pp. 25-28，(1998)

監修・ゲストエディタ
  1. I. Díaz and Y. Nojima, “Special Issue of the Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems,” Fuzzy Sets and Systems.
  2. I. Díaz and Y. Nojima, “New trends in aggregation and fusion of information,” International Journal of Approximate Reasoning.
  3. Y. Nojima, I. Díaz, and A. Inoue, “Special issue on selected papers from IFSA-SCIS2017,” Journal of Advanced Computational Intelligence and Intelligent Informatics.
  4. Z. Deng, J. Lu, D. Wu, K.-S. Choi, S. Sun, Y. Nojima, “Special issue on New Advances in Deep-Transfer Learning,” IEEE Trans. on Emerging Topics in Computational Intelligence.
  5. 串田淳一，佐藤寛之，渡邉真也，能島裕介，進化計算学会論文誌シンポジウム特集号，進化計算学会論文誌
  6. 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.
  7. 能島裕介，飯間等，「進化計算の新時代」特集号を企画して，システム／制御／情報，vol. 60, no. 7, p. 264, 2016.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 能島裕介 日本知能情報ファジィ学会誌 特集「進化計算の新しい展開」
  14. 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 Reviewers Other Committees
国内での活動