YNU Nakata Lab

Publication

Journals
  1. Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama, Keiki Takadama, Hisao Ishibuchi, and Masaya Nakata, Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems, IEEE Transactions on Evolutionary Computation, IEEE, Early Access. Paper
  2. Hiroki Shiraishi, Hisao Ishibuchi, and Masaya Nakata, A Class Inference Scheme With Dempster-Shafer Theory for Learning Fuzzy-Classifier Systems, ACM Transactions on Evolutionary Learning and Optimization, ACM, February 2025. Paper Code
  3. Ryudai Kato, Yuma Horaguchi, Masaya Nakata, Adaptive Selection of the Number of Generations for Model-based Search on K-RVEA, Transaction of the Japanese Society for Evolutionary Computation, Vol.15, No.1, pp.31-45, September 2024. Paper
  4. Yuma Horaguchi, Kei Nishihara and Masaya Nakata, Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems, Swarm and Evolutionary Computation, Elsevier, April 2024, Vol. 86, 101516. Paper Code
  5. Kei Nishihara and Masaya Nakata, Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems, Vol. 10, pp.3633–3656, Complex & Intelligent Systems, Springer, 2024. Paper Code
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  6. Takaharu Shoji, Masaki Kuriyama, and Masaya Nakata, Piecewise Symbolic Regression by Evolutionary Rule-based Learning with Genetic Programming, Transactions on Mathematical Modeling and its Applications, Vol 16, No.2, pp.36--49, IPSJ, October 2023. Paper
  7. Yuma Horaguchi, Takashi Ikeguchi, and Masaya Nakata, Classifier-based Pre-screening Mechanism for Surrogate-assisted Multi-Objective Evolutionary Algorithms, Transactions on Mathematical Modeling and its Applications, Vol.16, No.2, pp.23--35, IPSJ, October 2023. Paper
  8. Yusuke Hiruta, Kei Nishihara, Yuji Koguma, Masakazu Fujii, and Masaya Nakata, Automatic Construction of Loading Algorithms With Interactive Genetic Programming, IEEE Access, Vol.10, pp.125167-125180, IEEE, November 2022. Paper
  9. Rui Sugawara and Masaya Nakata, Theoretical Analysis of Accuracy-based Fitness on Learning Classifier Systems, IEEE Access, Vol.10, pp.64862-64872, IEEE, June 2022. Paper
  10. Takumi Sonoda and Masaya Nakata, Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multiobjective Problems, IEEE Transactions on Evolutionary Computation, Vol.26, No.6, pp.1581-1595, IEEE, March 2022. Paper Code
  11. Yushi Miyahara and Masaya Nakata, Hybrid Surrogate-Assisted Particle Swarm Optimization Based on Approximation and Classification Models, Transaction of the Japanese Society for Evolutionary Computation, Vol.12, No.3, pp.73-87, 2021. Paper
  12. Ryo Shiratori, Masaya Nakata, Kosuke Hayashi, and Toshihiko Baba, Particle swarm optimization of silicon photonic crystal waveguide transition, Optics Letters, Vol.46, No.8, pp.1904-1907, Optical Society of America, 2021. Paper
  13. Nishihara, Kei and Nakata, Masaya, Performance Improvement with Prior-validation Framework for Algorithmic Configuration on Self-adaptive Differential Evolution, Transactions on Mathematical Modeling and its Applications, Vol.14, No.3, pp.51--67, IPSJ, Aug 2021. Paper
  14. Yusuke Hiruta, Kei Nishihara, Yuji Koguma, Masakazu Fujii, and Masaya Nakata, Automated Construction of Transferable Loading Algorithm with Cartesian Genetic Programming, Transactions on Mathematical Modeling and its Applications, Vol.14, No.3, pp.11--26, IPSJ, Aug 2021. Paper
  15. Masaya Nakata and Will N. Browne, Learning Optimality Theory for Accuracy-based Learning Classifier System, IEEE Transactions on Evolutionary Computation, Vol.25, No.1, pp.61-74, IEEE, February 2021. Paper Code
  16. Fumito Uwano, Naoki Tatebe, Yusuke Tajima, Masaya Nakata, Tim Kovacs, and Keiki Takadama, Multi-Agent Cooperation Based on Reinforcement Learning with Internal Reward in Maze Problem, Journal of Control, Measurement and System Integration, Vol.11, No.4, pp.321-330, June 2018. Paper
  17. Masaya Nakata and Keiki Takadama, An Empirical Analysis of Action Map in Learning Classifier Systems, Journal of Control, Measurement, and System Integration, Vol.11, No.3, pp.239-248, May 2018. Paper
  18. Masya Nakata and Tomoki Hamagami, Revisit of Rule-Deletion Strategy for XCSAM Classifier System on Classification, Transaction of Institute of System, Control, and Engineering, Vol.30, No.7, pp.273-285, July 2017. Paper
  19. Masaya Nakata and Tomoki Hamagami, An Analysis of Rule Deletion Scheme in XCS on Reinforcement Learning Problem, Journal of Advanced Computational Intelligent Information, Vol.21, No.5, pp.876-884, September 2017. Paper
  20. Chaili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama, Approach to Clustering with Variance-Based XCS, Journal of Advanced Computational Intelligent Information, Vol.21, No.5, pp.885-893, September 2017. Paper
  21. Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, and Keiki Takadama, A Learning Classifier System that Adapts Accuracy Criterion, Transaction of the Japanese Society for Evolutionary Computation, Vol.6, No.2, pp.90--103, April 2016. Paper
  22. Masaya Nakata, Pier Luca Lanzi, and Keiki Takadama, Rule Reduction by Selection Strategy in XCS with Adaptive Action Map, Evolutionary Intelligence, Vol.8, No.2-3, pp.71-87, Springer, September 2015. Paper
  23. Masaya Nakata, Tim Kovacs, and Keiki Takadama, XCS-SL: A Rule-based Genetic Learning System for Sequence Labelling, Evolutionary Intelligence, Vol.8, No.2-3, pp.133-148, Springer, September 2015. Paper
  24. Yusuke Tajima, Masaya Nakata, Hiroyasu Matsushima, Yoshihiro Ichikawa, Keiji Sato, Kiyohiko Hattori, and Keiki Takadama, Evolutionary Algorithm for Uncertain Evaluation Function, New Mathematics and Natural Computation, Vol.11, No.2, pp.201-215, February 2015. Paper
  25. Masaya Nakata, Pier Luca Lanzi, Yusuke Tajima, and Keiki Takadama, Rule Reduction in Learning Classifier System Using Compact Genetic Algorithm, IPSJ Transactions on Mathematical Modeling and its Applications, Vol.7, No.2, pp.1--16, November 2014. Paper
  26. Masaya Nakata, Tomohiro Harada, Keiji Sato, Hiroyasu Matsushima, and Keiki Takadama, Performance Improvement by Identification-based XCS Using Predicted Reward, Transactions of the Society of Instrument and Control Engineers, Vol.48, No.11, pp.713--722, November 2012. Paper
  27. Masaya Nakata, Tomohiro Harada, Keiji Sato, Hiroyasu Matsushima, and Keiki Takadama, Promoting Generalization in Identification Based Learning Classifier System, Transactions of the Society of Instrument and Control Engineers, Vol.47, No.11, pp.581--590, November 2011. Paper

Preprints
  1. Yuma Horaguchi and Masaya Nakata, High-Dimensional Expensive Optimization by Classification-based Multiobjective Evolutionary Algorithm with Dimensionality Reduction, TechRxiv, IEEE, May 2023. Paper
  2. Yuma Horaguchi, Kei Nishihara, and Masaya Nakata, Evolutionary Multiobjective Optimization Assisted by Scalarization Function Approximation for High-Dimensional Expensive Problems, TechRxiv, IEEE, April 2023. Paper Code
  3. Takumi Sonoda and Masaya Nakata, Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems, TechRxiv, July 2021. Paper Code

Proceedings
  1. Ryotaro Nakahashi, Ryudai Kato, Yuma Horaguchi, and Masaya Nakata, Effective Utilization of Pre-evaluated Solutions for Surrogate-Assisted Multiobjective Evolutionary Algolthms, SICE Annual Conference 2024, SICE, September 2024.
  2. Ryo Fukami, Yuma Horaguchi, and Masaya Nakata, A Surrogate-Assisted Evolutionary Algorithm with Rough Set Theory for High-Dimensional Expensive Multi-objective Optimization Problems, Proceedings of SICE Annual Conference 2024, SICE, September 2024.
  3. Kei Nishihara and Masaya Nakata, A Surrogate-assisted Partial Optimization for Expensive Constrained Optimization Problems, Proceedings of 18th International Conference on Parallel Problem Solving from Nature (PPSN), pp.391–407, September 2024. Paper Code
  4. Hiroki Shiraishi, Rongguang Ye, Hisao Ishibuchi, and Masaya Nakata, A Variable-Length Fuzzy Set Representation for Learning Fuzzy-Classifier Systems, Proceedings of 18th International Conference on Parallel Problem Solving from Nature (PPSN), pp.386–402, September 2024. Paper
  5. Takashi Ikeguchi, Shun Sudo, Yuji Koguma, and Masaya Nakata, A Random Forest-Assisted Local Search for Expensive Permutation-based Combinatorial Optimization Problems, Proceedings of IEEE Congress on Evolutionary Computation 2024, pp.01-08, July 2024. Paper
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  6. Norihiro Kimoto, Yuma Horaguchi and Masaya Nakata, Oversampling-Guided Search for Evolutionary Multiobjective Optimization, Proceedings of IEEE Congress on Evolutionary Computation 2024, pp.01-08, July 2024. Paper
  7. Yuma Horaguchi and Masaya Nakata, A Dual Surrogate-based Evolutionary Algorithm for High-Dimensional Expensive Multiobjective Optimization Problems, Proceedings of IEEE Congress on Evolutionary Computation 2024, pp.01-08, July 2024. Paper Code
  8. Yuma Horaguchi and Masaya Nakata, High-Dimensional Expensive Optimization by Classification-based Multiobjective Evolutionary Algorithm with Dimensionality Reduction, Proceedings of SICE Annual Conference 2023, pp.1535-1542, SICE, September 2023. Paper Code
  9. Kei Nishihara and Masaya Nakata, Utilizing the Expected Gradient in Surrogate-assisted Evolutionary Algorithms, Proceedings of the Genetic and Evolutionary Computation Conference 2023 Companion, pp.447-450, July 2023. Paper
  10. Kei Nishihara and Masaya Nakata, Surrogate-assisted Differential Evolution with Adaptation of Training Data Selection Criterion, Proceedings of IEEE Symposium Series on Computational Intelligence 2022, pp.1675-1682, IEEE, Dec 2022. Code
  11. Koki Hamasaki and Masaya Nakata, Minimum Rule-Repair Algorithm for Supervised Learning Classifier Systems on Real-valued Classification Tasks, Proceedings of 8th International Conference on Metaheuristics and Nature Inspired computing, pp.111-120, 2021.
  12. Horiuchi Motoki and Nakata Masaya, Extended Learning Optimality Theory for the XCS classifier system on Multiple Reward Scheme,Proceedings of IEEE Symposium Series on Computational Intelligence 2021, IEEE, 2021.
  13. Yoshiki Nakamura, Motoki Horiuchi, and Masaya Nakata, Convergence Analysis of Rule-Generality on the XCS Classifier System,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.332-339, 2021.
  14. Kei Nishihara and Masaya Nakata, Comparison of Adaptive Differential Evolution Algorithms on the MOEA/D-DE Framework, Proceedings of IEEE Congress on Evolutionary Computation 2021, pp.161-168, IEEE, 2021.
  15. Takumi Sonoda and Masaya Nakata, MOEA/D-S3: MOEA/D using SVM-based Surrogates adjusted to Subproblems for Many objective optimization, Proceedings of IEEE Congress on Evolutionary Computation 2020, pp.E-24155, (8), IEEE, 2020.
  16. Kei Nishihara and Masaya Nakata, Competitive-Adaptive Algorithm-Tuning of Metaheuristics inspired by the Equilibrium Theory: A Case Study, Proceedings of IEEE Congress on Evolutionary Computation 2020, pp.E-24156, (8), IEEE, 2020.
  17. Motoki Horiuchi and Masaya Nakata, Self-adaptation of XCS learning parameters based on Learning theory, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.342-349, 2020.
  18. Masaya Nakata and Will Browne, How XCS Can Prevent Misdistinguishing Rule Accuracy: A Preliminary Study, Proceedings of the Genetic and Evolutionary Computation Conference 2019 Companion, pp.183-184, July 2019.
  19. Keiki Takadama, Daichi Yamazaki, Masaya Nakata, and Hiroyuki Sato, Complex-Valued-based Learning Classifier System for POMDP Environments, Proceedings of IEEE Congress on Evolutionary Computation 2019, pp.1852-1859, June 2019.
  20. Masaya Nakata, Will Browne, and Tomoki Hamagami, Theoretical adaptation of multiple rule-generation in XCS,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.482-489, ACM, July 2018.
  21. Kazuhisa Chiba, Yuhei Umeda, Naoki Hamada, Shinya Watanabe, Masaya Nakata, Kanako Yasue, Koji Suzuki, Takashi Atobe, Shigeru Kuchiishi, Kazuyuki Nakatakita, and Ken Ito, Determination of Temporal and Spatial Origination of Transonic Buffet via Unsteady Data Mining, Proceedings of AIAA Aerospace Sciences Meeting, 2018-0036, AIAA, January 2018.
  22. Hayato Sasaki, Masaya Nakata, Fumiya Hamatsu, and Tomoki Hamagami, Effect of parameter sharing for multimodal deep autoencoders,Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp.1966-1971, IEEE, October 2017.
  23. Kazuhica Chiba, Shinya Watanabe, Masaya Nakata, Yuhei Umeda, Naoki Hamada, Kanako Yasue, Koji Suzuki, Takashi Atobe, Shigeru Kuchiishi, Kazuyuki Nakatakita, and Ken Ito, An Attempt for Detecting Transonic Buffet Signature via Unsteady-Data Mining, Proceedings of the International Conference on Data Mining (DMIN17), pp.28--33, CSREA, July 2017.
  24. Kazuhisa Chiba and Masaya Nakata, From Extraction to Generation of Design Information - Paradigm Shift in Data Mining via Evolutionary Learning Classifier System, Procedia Computer Science, Vol.108, pp.1662-1671, Elsevier, June 2017. Paper
  25. Masaya Nakata, Will Browne, Tomoki Hamagami, and Keiki Takadama, Theoretical XCS parameter settings of learning accurate classifiers,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.473-480, ACM, July 2017.
  26. Masaya Nakata and Kazuhisa Chiba, Design Strategy Generation for a Sounding Hybrid Rocket via Evolutionary Rule-Based Data Mining System,Proceedings of Intelligent and Evolutionary Systems, pp.305-318, Springer, November 2016.
  27. Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyui Sato, Tim Kovacs, and Keiki Takadama, Variance-based Learning Classifier System without Convergence of Reward Estimation, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.67-68, ACM, July 2016.
  28. Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, and Keiki Takadama, A Modified Cuckoo Search Algorithm for Dynamic Optimization Problems,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.1754-1764, IEEE, July 2016.
  29. Tim Kovacs, Simon Rawles, Larry Bull, Masaya Nakata, Keiki Takadama, XCS-DH: Minimal Default Hierarchies in XCS,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.4747-4754, IEEE, July 2016.
  30. Kazuma Matsumoto, Rei Saito, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, and Keiki Takadama, Learning Classifier System with Deep Auto-encoder, Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.4739-4746, IEEE, July 2016.
  31. Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, and Keiki Takadama, Preventing incorrect opinion sharing with weighted relationship among agents,Proceedings of International Conference on Human Interface and the Management of Information, pp.50--62, Springer, July 2016.
  32. Fumito Uwano, Naoki Tatebe, Masaya Nakata, and Keiki Takadama and Kovacs, Tim, Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach, Proceedings of 9th EAI International Conference on Bio-inspired Information and Communications Technologies, pp.332--339, EAI, May 2016.
  33. Akinori Murata, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, and Keiki Takadama, Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation, Proceedings of 9th EAI International Conference on Bio-inspired Information and Communications Technologies, pp.340--347, EAI, May 2016.
  34. Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Will Browne, and Keiki Takadama, Keiki,How Should Learning Classifier Systems Cover A State-Action Space?,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.3012-3019, IEEE, May 2015.
  35. Keiki Takadama and Masaya Nakata, Extracting Both Generalized and Specialized Knowledge by XCS using Attribute Tracking and Feedback,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.3034-3041, IEEE, May 2015.
  36. Minato Sato, Kotaro Usui, Masaya Nakata, and Keiki Takadama, Detecting Shoplifting From Customer Behavior Data by Extended XCS-SL: Towards Feature Extraction on Class-Imbalanced Sequence Data,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.2981--2988, IEEE, May 2015.
  37. Masaya Nakata, Tim Kovacs, and Keiki Takadama, Messy Coding in the XCS Classifier System for Sequence Labelling,Proceedings of International Conference on Parallel Problem Solving from Nature, pp.191-200, Springer, September 2014.
  38. Masaya Nakata, Tim Kovacs, and Keiki Takadama, A Modified XCS Classifier System for Sequence Labelling,Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp.565-572, ACM, July 2014.
  39. Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, and Keiki Takadama, Complete action map or Best action map in Accuracy-based Reinforcement Learning Classifier Systems, Proceedings of Genetic and Evolutionary Computation Conference (CEC), pp.557--564, ACM, July 2014.
  40. Kotaro Usui, Masaya Nakata, and Keiki Takadama, Reusable knowledge by linkage-classifier in Accuracy-based Learning Classifier System,Proceedings of Sixth world congress on Nature and biologically inspired computing, pp.312-317, IEEE, July 2014.
  41. Takahiro Komine, Masaya Nakata, and Keiki Takadama, Archives-holding XCS Classifier System: A preliminary study,Proceedings of Sixth world congress on Nature and biologically inspired computing, pp.53-58, IEEE, July 2014.
  42. Yusuke Tajima, Masaya Nakata, and Keiki Takadama, Personalized real-time sleep stage remote monitoring system,Proceedings of 8th International Symposium on Medical Information and Communication Technology, pp.1-5, IEEE, April 2014.
  43. Yusuke Tajima, Masaya Nakata, Tomohiro Harada, Keiji Sato, and Keiki Takadama, Sleep stage estimation using synthesized data of heart rate and body movement, Proceedings of AAAI Spring Symposium, pp.63-68, AAAI, March 2014.
  44. Masaya Nakata, Pier Luca Lanzi, and Keiki Takadama, Selection Strategy for XCS with Adaptive Action Mapping, Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp.1085-1092, ACM, July 2013.
  45. Masaya Nakata, Pier Luca Lanzi, and Keiki Takadama, Simple Compact Genetic Algorithm for XCS, Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.1718-1723, IEEE, June 2013.
  46. Masaya Nakata, Pier Luca Lanzi, and Keiki Takadama, XCS with adaptive action mapping,Proceedings of 9th International Conference on Simulated Evolution and Learning, pp.138-147, Springer, December 2012.
  47. Masaya Masaya, Pier Luca Lanzi, Keiki Takadama, Enhancing Learning Capabilities by XCS with Best Action Mapping, Proceedings of International Conference on Parallel Problem Solving from Nature, pp.256-265, Springer, September 2012.
  48. Masaya Nakata, Fumiaki Sato, and Keiki Takadama, Towards Generalization by Identification-based XCS in Multi-steps Problem, Third world congress on Nature and biologically inspired computing, pp.389-394, IEEE, October 2011.