Naoki Masuyama, Ph.D.

Associate Professor

Computational Intelligence Laboratory
Intelligent Informatics
Department of Core Informatics
Graduate School of Informatics
Osaka Metropolitan University


E-mail: masuyama (at) omu.ac.jp
Address: 1-1 Gakuen-cho, Sakai, Osaka 599-8531, Japan
Phone: +81-72-254-9198
FAX: +81-72-254-9825

GitHub
Google Scholar
dblp computer science bibliography






I graduated from Nihon University, Chiba, Japan in 2010, and I received the M.E. degree from Tokyo Metropolitan University, Tokyo, Japan in 2012. Since April 2016, I obtained my Ph.D. degree from Faculty of Computer Science and Information Technology, University of Malaya, Malaysia.
After obtaining my Ph.D., I worked as a postdoctoral research fellow from August 2016 until August 2017 at University of Malaya, Malaysia. From October 2017 until March 2022, I worked an Assistant Professor in Graduate School of Engineering, Osaka Prefecture University, Japan. From April 2022 to September 2022, I worked an Assistant Professor in Graduate School of Core Informatics, Osaka Metropolitan University, Japan. Currently, I am working as an Associate Professor in Graduate School of Informatics, Osaka Metropolitan University, Japan.

Education
March 2013 – April 2016 ： Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
(Advisor: Prof. Dr. Chu Kiong Loo)
(Thesis title：Quantum-inspired associative memories for incorporating emotion in a humanoid)
April 2010 – March 2012 ： Department of Mechanical Systems Engineering, Graduate School of Systems Design, Tokyo Metropolitan University, Tokyo, Japan.
(Advisor: Prof. Dr. Naoyuki Kubota)
April 2006 – March 2010 ： Department of Aerospace Engineering, Graduate School of Science and Technology, Nihon University, Chiba, Japan.

Professional
October 2022 – Present ： Associate Professor, Graduate School of Informatics, Department of Informatics, Osaka Metropolitan University, Japan.
April 2022 – September 2022 ： Assistant Professor, Graduate School of Informatics, Department of Informatics, Osaka Metropolitan University, Japan.
October 2017 – March 2022 ： Assistant Professor, Graduate School of Engineering, Osaka Prefecture University, Japan.
August 2016 – August 2017 ： Post Doctoral Research Fellow, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia.
April 2012 – June 2016 ： Research Assistant, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia.

Awards Grant and Fund


Book Chapters

    2024
  1. T. Obo, N. Kubota, Y. Toda, and N. Masuyama, "Fast multi-scale batch-learning growing neural gas," in: L. Kovács, T. Haidegger, A. Szakál (Eds.), "Recent Advances in Intelligent Engineering: Volume Dedicated to Imre J. Rudas’ Seventy-Fifth Birthday", Springer, Cham, Switzerland, 2024, pp. 13-33.

Journal Papers

(in English)
    2024
  1. 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, September 2024. Code Ocean
  2. Y. Toda, and N. Masuyama, "Adaptive resonance theory-based global topological map building for an autonomous mobile robot," IEEE Access, vol. 12, pp. 111371-111385, August 2024.
  3. N. B. Aji, Kurnianingsih, N. Masuyama, and Y. Nojima, "CNN-LSTM for heartbeat sound classification," International Journal on Informatics Visualization, vol. 8, no. 2, pp. 735-741, May 2024.

    4. 2023
  4. F. Dawood, N. Masuyama, and C. K. Loo, "Developmental cognitive architecture with continual learning through self-organizing multimodal perception coordination," 2023, Under Review.
  5. 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," 2023, Under Review. Code Ocean
  6. 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, November 2023.
  7. 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, 2023. Code
  8. 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. Code Ocean
  9. 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, #109005, March 2023.
  10. Z. Liu, G. A. Tahir, N. Masuyama, H. A. Kakudi, Z. Fu, and K. Pasupa, "Error-output recurrent multi-layer kernel reservoir network for electricity load time series forecasting," Engineering Application of Artificial Intelligence, vol. 117, Part A, #105611, January 2023.

    11. 2022
  11. Y. Okazaki, Y. Fujita, H. Murata, I. Yamada, N. Masuyama, Y. Nojima, H. Ikeno, and S. Yagi, "Composition-designed high entropy perovskite oxides for oxygen evolution catalysis," Chemistry of Materials, vol. 34, no. 24, pp. 10973-10981, December 2022.
  12. N. Masuyama, N. Amako, Y. Yamada, Y. Nojima, and H. Ishibuchi, "Adaptive resonance theory-based clustering with a divisive hierarchical structure capable of continual learning," IEEE Access, vol. 10, pp. 68042-68056, June 2022.

    13. 2020
  13. 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 Transactions on Evolutionary Computation, vol. 24, no. 3, pp. 551-565, June 2020.
  14. 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.

    15. 2019
  15. 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, pp. 76920-76936, December 2019.
  16. H. A. Kakudi, C. K. Loo, F. M. Moy, and N. Masuyama, "Diagnosing metabolic syndrome using genetically optimised Bayesian ARTMAP," IEEE Access, vol. 7, pp. 8437-8453, December 2019.
  17. N. Masuyama, C. K. Loo, and S. Wermter, "A kernel Bayesian adaptive resonance theory with a topological structure," International Journal of Neural Systems, vol. 29, no. 5, #1850052, June 2019.

    18. 2018
  18. Z. Liu, C. K. Loo, N. Masuyama, and K. Pasupa, "Recurrent kernel extreme reservoir machine for time series prediction," IEEE Access, vol. 6, pp. 19583-19596, December 2018.
  19. 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.
  20. N. Masuyama, C. K. Loo, and F. Dawood, "Kernel Bayesian ART and ARTMAP," Neural Networks, vol. 98, pp. 76-86, February 2018.
  21. N. Masuyama, C. K. Loo, and M. Seera, "Personality affected robotic emotional model with associative memory for human-robot interaction," Neurocomputing, vol. 272, pp. 213-225, January 2018.
  22. N. Masuyama, C. K. Loo, M. Seera, and N. Kubota, "Quantum-inspired multidirectional associative memory with a self-convergent iterative learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 1058-1068, April 2018.

    23. 2017
  23. N. Masuyama, Md. N. Islam, M. Seera, and C. K. Loo, "Application of emotion affected associative memory based on mood congruency effects for a humanoid," Neural Computing and Applications, vol. 28, no. 4, pp. 737-752, April 2017.

    24. 2014
  24. N. Masuyama, C. K. Loo, and N. Kubota, "Quantum-inspired bidirectional associative memory for human-robot communication," International Journal of Humanoid Robotics, vol. 11, no. 2, #1450006, June 2014.
(in Japanese)
    2024
  1. 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, 知能と情報（日本知能情報ファジィ学会誌）, vol. 36, no. 1, pp. 565-570, 2024．
  2. 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, 知能と情報（日本知能情報ファジィ学会誌）, vol. 36, no. 1, pp. 543-549, 2024．

    3. 2021
  3. 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, 知能と情報（日本知能情報ファジィ学会誌）, vol. 33, no. 1, pp. 525-530, 2021．
  4. 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, 知能と情報（日本知能情報ファジィ学会誌）, vol. 33, no. 1, pp. 531-536, 2021．
  5. 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, 知能と情報（日本知能情報ファジィ学会誌）, vol. 33, no. 1, pp. 537-542, 2021．
  6. 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, 知能と情報（日本知能情報ファジィ学会誌）, vol. 33, no. 1, pp. 543-548, 2021．

    7. 2020
  7. 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, 知能と情報（日本知能情報ファジィ学会誌）, vol. 32, no. 1, pp. 501-506, 2020.
  8. 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, 知能と情報（日本知能情報ファジィ学会誌）, vol. 32, no. 1, pp. 512-517, 2020.

Conference Papers
    2024
  1. 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.
  2. 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 the 2024 International Conference on Fuzzy Theory and Its Applications (iFuzzy 2024), Kagawa, Japan, August 10-13, 2024.
  3. R. Fuerst, N. Masuyama, and Y. Nojima, "Hierarchical fuzzy classifier design using a reject option," in Proc. of the 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-7, Yokohama, Japan, June 30 - July 5, 2024.
  4. T. Konishi, N. Masuyama, J. Casillas, and Y. Nojima, "Fairness-aware classifier design via multi-objective fuzzy genetics-based machine learning," in Proc. of the 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8, Yokohama, Japan, June 30 - July 5, 2024.
  5. K. Tashiro, N. Masuyama, and Y. Nojima, "A growing hierarchical clustering algorithm via parameter-free adaptive resonance theory," in Proc. of the 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1-6, Yokohama, Japan, June 30 - July 5, 2024. Code
  6. T. Kinoshita, N. Masuyama, and Y. Nojima, "A federated data-driven multiobjective evolutionary algorithm via continual learnable clustering," in Proc. of the 2024 IEEE Congress on Evolutionary Computation (CEC), pp. 1-7, Yokohama, Japan, June 30 - July 5, 2024.
  7. 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.

    8. 2023
  8. T. Kinoshita, N. Masuyama, and Y. Nojima, "Riesz s-energy indicators for diversity assessment of multiobjective evolutionary algorithms," in Proc. of the 24th International Symposium on Advanced Intelligent Systems (ISIS), 7 pages, Gwangju, Korea, December 6-9, 2023.
  9. 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 the 24th International Symposium on Advanced Intelligent Systems (ISIS), 6 pages, Gwangju, Korea, December 6-9, 2023.
  10. 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)
  11. Y. Nojima, K. Kawano, H. Shimahara, E. Vernon, N. Masuyama, and H. Ishibuchi, "Fuzzy classifiers with a two-stage reject option," in Proc. of the 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-6, Songdo Incheon, Korea, Aug. 13-17, 2023.
  12. 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.
  13. T. Takebayashi, N. Masuyama, Y. Nojima, "Adaptive resonance theory-based topological clustering with node deletion mechanism for evolving stream data," in Proc. of the International Conference on Machine Learning and Cybernetics and the International Conference on Wavelet Analysis and Pattern Recognition 2023 (ICMLC&ICWAPR), #4036, Adelaide, Australia, 9-11 July, 2023.

    14. 2022
  14. T. Konishi, N. Masuyama, and Y. Nojima, "Effects of accuracy-based single-objective optimization in multiobjective fuzzy genetics-based machine learning," in Proc. of the 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.
  15. T. Kinoshita, N. Masuyama, and Y. Nojima, "Search process analysis of multiobjective evolutionary algorithms using convergence-diversity diagram," in Proc. of the 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 received IEEE CIS Japan Chapter Young Researcher Award)
  16. E. M. Vernon, N. Masuyama, and Y. Nojima, "Error-reject tradeoff analysis on two-stage classifier design with a reject option," in Proc. of the World Automation Congress (WAC 2022), pp. 312-317, Texas, USA, October 11-15, 2022.
  17. M. Yano, N. Masuyama, and Y. Nojima, "Behavior analysis of constrained multiobjective evolutionary algorithms using scalable constrained multi-modal distance minimization problems," in Proc. of the World Automation Congress (WAC 2022), pp. 174-179, Texas, USA, October 11-15, 2022.
  18. Y. Omozaki, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Evolutionary multiobjective multi-tasking for fuzzy genetics-based machine learning in multi-label classification," in Proc. of the 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8, Padua, Italy, July 18-23, 2022, doi: 10.1109/FUZZ-IEEE55066.2022.9882681.
  19. N. Masuyama, Y. Nojima, H. Ishibuchi, and Z. Liu, "Adaptive resonance theory-based clustering for handling mixed data," in Proc. of the 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, Padua, Italy, July 18-23, 2022, doi: 10.1109/IJCNN55064.2022.9892060.
  20. T. Kinoshita, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Analytical methods to separately evaluate convergence and diversity for multi-objective optimization," in Proc. of the 14th International Conference of Metaheuristics (MIC 2022), pp. 172-186, Syracuse, Italy, July 11-14, 2022.

    21. 2021
  21. 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," in Proc. of the 22nd International Symposium on Advanced Intelligent Systems (ISIS 2021), Online, December 15-18, 2021.
  22. Y. Yamada, N. Amako, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Hierarchical topological clustering with automatic parameter estimation," in Proc. of the 22nd International Symposium on Advanced Intelligent Systems (ISIS 2021), Online, December 15-18, 2021.
  23. V. Villin, N. Masuyama, and Y. Nojima, "Effects of different optimization formulations in evolutionary reinforcement learning on diverse behavior generation," in Proc. of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI 2021), pp. 1-8, Virtual, December 4-7, 2021, doi: 10.1109/SSCI50451.2021.9659949.
  24. 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," in Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2021), Virtual, October 17-20, 2021, doi: 10.1109/SMC52423.2021.9658818.

    25. 2020
  25. N. Masuyama, Y. Nojima, C. K. Loo, and H. Ishibuchi, "Multi-label classification based on adaptive resonance theory," in Proc. of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI 2020), pp. 1913-1920, Canberra, Australia, December 1-4, 2020.
  26. Y. Yamada, N. Masuyama, N. Amako, Y. Nojima, C. K. Loo, and H. Ishibuchi, "Divisive hierarchical clustering based on adaptive resonance theory," in Proc. of the 2020 International Symposium on Community-centric Systems (CcS 2020), pp. 1-6, Tokyo, Japan, September 23-26, 2020, doi:10.1109/CcS49175.2020.9231474.
  27. N. Amako, N. Masuyama, C. K. Loo, Y. Nojima, Y. Liu, and H. Ishibuchi, "Multilayer clustering based on adaptive resonance theory for noisy environments," in Proc. of the 2020 International Joint Conference on Neural Networks (IJCNN 2020), pp. 1-8, Glasgow, UK, July 19-24, 2020, doi:10.1109/IJCNN48605.2020.9207071.
  28. 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," in Proc. of the 2020 IEEE Congress on Evolutionary Computation (CEC 2020), pp. 1-8, Glasgow, UK, July 19-24, 2020, doi:10.1109/CEC48606.2020.9185871.
  29. Y. Liu, H. Ishibuchi, G. G. Yen, Y. Nojima, N. Masuyama, and Y. Han, "On the normalization in evolutionary multi-modal multi-objective optimization," in Proc. of the 2020 IEEE Congress on Evolutionary Computation (CEC 2020), pp. 1-8, Glasgow, UK, July 19-24, 2020, doi:10.1109/CEC48606.2020.9185899.
  30. Y. Omozaki, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Multiobjective fuzzy genetics-based machine learning for multi-label classification," in Proc. of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020), pp. 1-8, Glasgow, UK, July 19-24, 2020, doi:10.1109/FUZZ48607.2020.9177804. (Best Student Paper Award)
  31. 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," in Proc. of the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), pp. 507-515, Cancun, Mexico, July 8-12, 2020.
  32. H. Ishibuchi, T. Matsumoto, N. Masuyama, and Y. Nojima, "Many-objective problems are not always difficult for Pareto dominance-based evolutionary algorithms," in Proc. of the 24th European Conference on Artificial Intelligence (ECAI 2020), pp. 291-298, Santiago, Spain, June 8-12, 2020.

    33. 2019
  33. 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," in Proc. of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019), pp. 1857-1864, Xiamen, China, December 6-9, 2019.
  34. N. Masuyama, C. K. Loo, H. Ishibuchi, N. Amako, Y. Nojima, and Y. Liu, "Fast topological adaptive resonance theory based on correntropy induced metric," in Proc. of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019), pp. 2215-2221, Xiamen, China, December 6-9, 2019.
  35. 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," in Proc. of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019), pp. 2681-2688, Xiamen, China, December 6-9, 2019.
  36. Y. Omozaki, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Development of a GUI tool for FML-based fuzzy system modeling," in Proc. of the 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.
  37. T. Fukase, N. Masuyama, Y. Nojima, Y. Liu, and H. Ishibuchi, "Dots-type constrained multiobjective distance minimization problems," in Proc. of the 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.
  38. Y. Nojima, T. Fukase, Y. Liu, N. Masuyama, and H. Ishibuchi, "Constrained multiobjective distance minimization problems," in Proc. of the 2019 Genetic and Evolutionary Computation Conference, pp. 586-594, Prague, Czech Republic, July 13-17, 2019.
  39. Y. Liu, H. Ishibuchi, Y. Nojima, N. Masuyama, and Y. Han, "Searching for local Pareto optimal solutions: A case study on polygon-based problems," in Proc. of the 2019 IEEE Congress on Evolutionary Computation, pp. 873-880, Wellington, New Zealand, June 10-13, 2019.
  40. T. Matsumoto, N. Masuyama, Y. Nojima, and H. Ishibuchi, "A multiobjective test suite with hexagon Pareto fronts and various feasible regions," in Proc. of the 2019 IEEE Congress on Evolutionary Computation, pp. 2059-2066, Wellington, New Zealand, June 10-13, 2019.
  41. 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," in Proc. of the 2019 IEEE Congress on Evolutionary Computation, pp. 2435-2442, Wellington, New Zealand, June 10-13, 2019.
  42. H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, "Comparison of hypervolume, IGD and IGD+ from the viewpoint of optimal distributions of solutions," in Proc. of the 10th International Conference on Evolutionary Multi-Criterion Optimization, pp. 332-345, East Lansing, USA, March 10-13, 2019. (Springer Best Paper Award - 1st Prize)

    43. 2018
  43. Y. Irie, N. Masuyama, Y. Nojima, and H. Ishibuchi, "A preliminary study of Michigan-style fuzzy genetics-based machine learning for class incremental problems," in Proc. of the 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, pp. 713-717, Toyama, Japan, December 5-8, 2018.
  44. G. C. Lee, C. K. Loo, and N. Masuyama, "Parameters estimation in topological kernel Bayesian ART using multi-objective particle swarm optimization," in Proc. of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018), pp. 1595-1601, Bangalore, India, November 18-21, 2018.
  45. T. Matsumoto, N. Masuyama, Y. Nojima, and H. Ishibuchi, "Performance comparison of multiobjective evolutionary algorithms on problems with partially different properties from popular scalable test suites," in Proc. of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2018), pp. 765-770, Miyazaki, Japan, October 7-10, 2018.
  46. Y. Nojima, Y. Tanigaki, N. Masuyama, and H. Ishibuchi, "Multiobjective evolutionary data mining for performance improvement of evolutionary multiobjective optimization," in Proc. of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2018), pp. 741-746, Miyazaki, Japan, October 7-10, 2018.
  47. Y. Liu, H. Ishibuchi, Y. Nojima, N. Masuyama, and K. Shang, "Improving 1by1EA to handle various shapes of Pareto fronts," in Proc. of the 15th International Conference on Parallel Problem Solving from Nature (PPSN 2018), pp. 311-322, Coimbra, Portugal, September 8-12, 2018.
  48. Y. Liu, H. Ishibuchi, Y. Nojima, N. Masuyama, and K. Shang, "A double-niched evolutionary algorithm and its behaviors on polygon-based problems," in Proc. of the 15th International Conference on Parallel Problem Solving from Nature (PPSN 2018), pp. 262-273, Coimbra, Portugal, September 8-12, 2018.
  49. H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, "Use of two reference points in hypervolume-based evolutionary multiobjective optimization algorithms," in Proc. of the 15th International Conference on Parallel Problem Solving from Nature (PPSN 2018), pp. 384-396, Coimbra, Portugal, September 8-12, 2018.
  50. H. Ishibuchi, T. Fukase, N. Masuyama, and Y. Nojima, "Dual-grid model of MOEA/D for evolutionary constrained multiobjective optimization," in Proc. of the 2018 Genetic and Evolutionary Computation Conference (GECCO 2018), pp.665-672, Kyoto, Japan, July 15-19, 2018.
  51. H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, "Dynamic specification of a reference point for hypervolume calculation in SMS-EMOA," in Proc. of the 2018 IEEE Congress on Evolutionary Computation (CEC 2018), pp.701-708, Rio de Janeiro, Brazil, July 8-13, 2018.
  52. N. Masuyama, C. K. Loo, H. Ishibuchi, Y. Nojima, and Y. Liu, "Topological kernel Bayesian ARTMAP," in Proc. of the World Automation Congress (WAC 2018), pp. 294-299, Washington, USA, June 3-6, 2018.

    53. 2017
  53. Z. Liu, C. K. Loo, N. Masuyama, and K. Pasupa, "Multiple steps time series prediction by a novel recurrent kernel extreme learning machine approach," in Proc. of 9th International Conference on Information Technology and Electrical Engineering (ICITEE 2017), SIG5.5, Phuket, Thailand, October 12-13, 2017.

    54. 2016
  54. C. W. Hong, C. K. Loo, and N. Masuyama, "Multi-channel Bayesian ART for robot fusion perception," in Proc. of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI 2016), pp. 1-5, Athens, Greece, December 6-9, 2016. (DOI: 10.1109/SSCI.2016.7850240)
  55. N. Masuyama, and C. K. Loo, "Growing neural gas with correntropy induced metric," in Proc. of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI 2016), pp. 1-7, Athens, Greece, December 6-9, 2016. (DOI: 10.1109/SSCI.2016.7850247)
  56. N. Masuyama, and C. K. Loo, "An iterative incremental learning algorithm for complex-valued Hopfield associative memory," in Proc. of the 23rd International Conference on Neural Information Processing (ICONIP 2016), pp. 423-431, Kyoto, Japan, October 16-21, 2016.

    57. 2015
  57. Z. Rasool, N. Masuyama, Md. N. Islam, and C. K. Loo, "Empathic interaction using the computational emotion model," in Proc. of the 2015 IEEE Symposium Series on Computational Intelligence (SSCI 2015), pp. 109–116, Cape Town, South Africa, December 7-10, 2015.
  58. N. Masuyama, and C. K. Loo, "Robotic emotional model with personality factors based on pleasant-arousal scaling model," in Proc. of the IEEE 24th International Symposium on Robot and Human Interactive Communication (RO-MAN 2015), pp. 19-24, Kobe, Japan, August 31-September 4, 2015. (Best Paper Award Finalist, 5 papers out of 138 papers)
  59. N. Masuyama, and C. K. Loo, "Quantum-inspired complex-valued multidirectional associative memory," in Proc. of the 2015 International Joint Conference on Neural Networks (IJCNN 2015), pp. 1-8, Killarney, Ireland, July 11-16, 2015. (DOI: 10.1109/IJCNN.2015.7280403)

    60. 2014
  60. N. Masuyama, Md. N. Islam, and C. K. Loo, "Affective communication robot partners using associative memory with mood congruency effects," in Proc. of the 2014 IEEE Symposium Series on Computational Intelligence (SSCI 2014), pp. 1-8, Orlando, USA, December 9-12, 2014. (DOI: 10.1109/RIISS.2014.7009178)
  61. N. Masuyama, and C. K. Loo, "Quantum-inspired multidirectional associative memory for human-robot interaction system," in Proc. of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014), pp. 1757-1764, Beijing, China, July 6-11, 2014.

    62. 2013
  62. N. Masuyama, C. K. Loo, and N. Kubota, "Human-robot interaction system with quantum-inspired bidirectional associative memory," in Proc. of Second International Conference on Robot, Vision and Signal Processing (RVSP 2013), pp. 66-71, Kitakyushu, Japan, December 10-12, 2013.
  63. N. Masuyama, C. K. Loo, and N. Kubota, "Quantum mechanics inspired bidirectional associative memory for human robot interaction," in Proc. of the 3rd International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII 2013), GS1-5, Shanghai, China, October 18-21, 2013.

    64. 2012
  64. N. Masuyama, C. S. Chan, N. Kubota, and J. Woo, "Computational intelligence for human interactive communication of robot partners", in Proc. of the 12th Pacific Rim International Conference on Trends in Artificial Intelligence (PRICAI 2012), pp. 771-776, Kuching, Malaysia, September 3-7, 2012.

Others

    2024
  1. 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)．
  2. 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)．
  3. 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)．
  4. 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)．
  5. 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に保持するデータの選択手法の調査，第7回継続学習と知能の創発研究会講演論文集，岡山，6月，(2024)．

    6. 2023
  6. 鳥越大貴，増山直輝，能島裕介，パラメータ設定の不要な適応共鳴理論に基づく階層的クラスタリング手法の性能比較，第6回継続学習と知能の創発研究会講演論文集，東京，12月，(2023)．
  7. 田代一貴，増山直輝，能島裕介，適応共鳴理論に基づく階層的クラスタリング手法の比較検討，第6回継続学習と知能の創発研究会講演論文集，東京，12月，(2023)．
  8. 増山直輝，自己教師あり継続学習のためのクラスタリングによるリプレイバッファ選択方法の改良，第6回継続学習と知能の創発研究会講演論文集，東京，12月，(2023)．
  9. 上田裕也，増山直輝，能島裕介，適応共鳴理論に基づく連合クラスタリング手法の比較検討，第6回継続学習と知能の創発研究会講演論文集，東京，12月，(2023)．
  10. 西川毅，増山直輝，能島裕介，適応共鳴理論に基づくマルチラベル量質混在データのためのクラスタリング手法の改良，第6回継続学習と知能の創発研究会講演論文集，東京，12月，(2023)．
  11. 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回進化計算研究会講演論文集，pp. 49-56，福知山，9月 (2023).
  12. 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)．
  13. 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，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)．
  14. 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)．
  15. 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)．
  16. 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)．
  17. 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5回継続学習と知能の創発研究会講演論文集，大阪，6月，(2023)．
  18. 扇野雄太，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を用いたマルチラベル識別におけるラベル学習手法の改良，第5回継続学習と知能の創発研究会講演論文集，大阪，6月，(2023)．

    19. 2022
  19. 竹林章宜，増山直輝，能島裕介，警戒パラメータの推定が可能な適応共鳴理論に基づくトポロジカルクラスタリング手法，第4回継続学習と知能の創発研究会講演論文集，大阪，12月，(2022)．
  20. 坪田一希，増山直輝，能島裕介，トポロジカルクラスタリングに基づく識別器の量質混在データへの対応，第4回継続学習と知能の創発研究会講演論文集，大阪，12月，(2022)．
  21. 田代一貴，増山直輝，能島裕介，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および階層構造の可視化，第4回継続学習と知能の創発研究会講演論文集，大阪，12月，(2022)．
  22. 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）
  23. 木下貴登，増山直輝，能島裕介，Convergence-Diversity Diagramの探索過程分析への拡張，第22回進化計算学会研究会講演論文集，pp. 14-19，東京＋オンライン，9月 (2022).
  24. 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).
  25. 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)．
  26. 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)．
  27. 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)．
  28. 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)．
  29. 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)．
  30. 田代一貴，増山直輝，能島裕介，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3回継続学習と知能の創発研究会講演論文集，東京，6月，(2022)．
  31. 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手法によるマルチラベル識別器の改良，第3回継続学習と知能の創発研究会講演論文集，東京，6月，(2022)．

    32. 2021
  32. 尼子就都，増山直輝，能島裕介，石渕久生, 警戒パラメータの適応的な推定が可能な適応共鳴理論に基づくトポロジカルクラスタリング手法，第2回継続学習と知能の創発研究会講演論文集，オンライン，12月，(2021)．
  33. 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).
  34. 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).
  35. 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).
  36. 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).
  37. 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).
  38. 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).
  39. 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，ファジィシステムシンポジウム2021講演論文集, オンライン, 9月, (2021)．
  40. 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)．
  41. 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，ファジィシステムシンポジウム2021講演論文集, オンライン, 9月, (2021)．
  42. 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，ファジィシステムシンポジウム2021講演論文集, オンライン, 9月, (2021)．
  43. 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)．
  44. 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1回継続学習と知能の創発研究会講演論文集，オンライン，8月，(2021)．
  45. 山田友菜，尼子就都，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，第1回継続学習と知能の創発研究会講演論文集，オンライン，8月，(2021)．
  46. 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1回継続学習と知能の創発研究会講演論文集，オンライン，8月，(2021)．
  47. 坪田一希，尼子就都，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トポロジカルクラスタリングに基づく識別器設計，第1回継続学習と知能の創発研究会講演論文集，オンライン，8月，(2021)．
  48. 増山直輝，適応共鳴理論によるトポロジカルクラスタリングとその展開，知能と情報（日本知能情報ファジィ学会誌），vol. 33, no. 2, pp. 48-56, 2021.

    49. 2020
  49. 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)．
  50. 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)．
  51. 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).
  52. 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，ファジィシステムシンポジウム2020講演論文集，pp. 53-58，福岡，9月 (2020).
  53. 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，ファジィシステムシンポジウム2020講演論文集，pp. 47-52，福岡，9月 (2020).
  54. 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，ファジィシステムシンポジウム2020講演論文集，pp. 367-372，福岡，9月 (2020)．
  55. 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，ファジィシステムシンポジウム2020講演論文集，pp. 441-446，福岡，9月 (2020).

    56. 2019
  56. 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)．
  57. 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)．
  58. 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).
  59. 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).
  60. 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，ファジィシステムシンポジウム2019講演論文集，pp. 121 -126，大阪，8月 (2019)．
  61. 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，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)．
  62. 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，ファジィシステムシンポジウム2019講演論文集，pp. 53-58，大阪，8月 (2019).
  63. 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，ファジィシステムシンポジウム2019講演論文集，pp. 47-52，大阪，8月 (2019)．
  64. 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，ファジィシステムシンポジウム2019講演論文集，pp. 1-6，大阪，8月 (2019).

    65. 2018
  65. 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)
  66. 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)
  67. 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)
  68. 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," in Proc. of the 2018 JPNSEC International Workshop on Evolutionary Computation, 2 pages, Shenzhen, China, September 2018.
  69. 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," in Proc. of the 2018 JPNSEC International Workshop on Evolutionary Computation, 6 pages, Shenzhen, China, September 2018.
  70. R. Hashimoto, N. Masuyama, Y. Nojima, and H. Ishibuchi, "MOEA/D for multi-task multi-objective optimization," in Proc. of the 2018 JPNSEC International Workshop on Evolutionary Computation, 2 pages, Shenzhen, China, September 2018.
  71. 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)
  72. Ryuichi Hashiomoto, Hisao Ishibuchi, Naoki Masuyama, and Yusuke Nojima, "Analysis of evolutionary multi-tasking as an island model," In Companion of Genetic and Evolutionary Computation Conference, pp. 1894-1897, Kyoto, Japan, July 15-19, 2018.

    73. 2017
  73. 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).
  74. 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).
  75. 増山直輝，チューキョンルー，個性の影響を受ける情動モデルによる情動的連想記憶モデル，インテリジェント・システム・シンポジウム2017講演論文集，pp. 73-78，岡山，11月 (2017).