YNU Nakata Lab

Publications

Journals
  1. 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]
  2. Kei Nishihara and Masaya Nakata, Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems, Complex & Intelligent Systems, Springer, 2024. [paper] [code]
  3. 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]
  4. 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]
  5. 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]
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  6. 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]
  7. 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]
  8. Yushi Miyahara, 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]
  9. 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]
  10. 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]
  11. Hiruta, Yusuke and Nishihara, Kei and Koguma, Yuji and Fujii, Masakazu and Nakata, Masaya, 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. Tatsumi, Takato and Komine, Takahiro and Nakata, Masaya and Sato, Hiroyuki and Takadama, Keiki, 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]
  20. 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]
  21. Masaya Nakata, Tim Kovacs, 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]
  22. 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]
  23. Nakata, Masaya and Pier Luca Lanzi and Tajima, Yusuke and Takadama, Keiki, 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]
  24. Nakata, Masaya and Harada, Tomohiro and Sato, Keiji and Matsushima, Hiroyasu and Takadama, Keiki, 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]
  25. Nakata, Masaya and Harada, Tomohiro and Sato, Keiji and Matsushima, Hiroyasu and Takadama, Keiki, 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, 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, Masaya Nakata, Evolutionary Multiobjective Optimization Assisted by Scalarization Function Approximation for High-Dimensional Expensive Problems, TechRxiv, IEEE, April 2023. [paper] [code]
  3. Takumi Sonoda, Masaya Nakata, Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems, TechRxiv, IEEE, July 2021. [paper] [code]

Proceedings
  1. Takashi Ikeguchi, Shun Sudo, Yuji Koguma and Masaya Nakata, A Random Forest-Assisted Local Search for Expensive Permutation-based Combinatorial Optimization Problems, IEEE World Congress on Computational Intelligence 2024.
  2. Norihiro Kimoto, Yuma Horaguchi and Masaya Nakata, Oversampling-Guided Search for Evolutionary Multiobjective Optimization, IEEE World Congress on Computational Intelligence 2024.
  3. Yuma Horaguchi and Masaya Nakata, A Dual Surrogate-based Evolutionary Algorithm for High-Dimensional Expensive Multiobjective Optimization Problems, IEEE World Congress on Computational Intelligence 2024.
  4. Yuma Horaguchi and Masaya Nakata, High-Dimensional Expensive Optimization by Classification-based Multiobjective Evolutionary Algorithm with Dimensionality Reduction, SICE Annual Conference 2023, pp1535-1542, SICE, September 2023.
  5. Kei Nishihara and Masaya Nakata, Utilizing the Expected Gradient in Surrogate-assisted Evolutionary Algorithms, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion, pp.447-450, July 2023. [paper]
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  6. Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama, Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.548-557, July 2023. [paper]
  7. Kei Nishihara, Masaya Nakata, Surrogate-assisted Differential Evolution with Adaptation of Training Data Selection Criterion, IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022), pp.1675-1682, IEEE, Dec 2022.
  8. Koki Hamasaki, Masaya Nakata, Minimum Rule-Repair Algorithm for Supervised Learning Classifier Systems on Real-valued Classification Tasks,Proceedings of META’2021 8 th International Conference on Metaheuristics and Nature Inspired computing, pp.111-120, META, 2021.
  9. Horiuchi Motoki and Nakata Masaya,Extended Learning Optimality Theory for the XCS classifier system on Multiple Reward Scheme,IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), IEEE, 2021.
  10. Nakamura, Yoshiki and Horiuchi, Motoki and Nakata, Masaya,Convergence Analysis of Rule-Generality on the XCS Classifier System,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.332-339, 2021.
  11. Nishihara, Kei and Nakata, Masaya,Comparison of Adaptive Differential Evolution Algorithms on the MOEA/D-DE Framework,IEEE Congress on Evolutionary Computation (CEC), pp.161-168, IEEE, 2021.
  12. Sonoda, Takumi and Nakata, Masaya,MOEA/D-S3: MOEA/D using SVM-based Surrogates adjusted to Subproblems for Many objective optimization,IEEE Congress on Evolutionary Computation (CEC), pp.E-24155, (8), IEEE, 2020.
  13. Nishihara, Kei and Nakata, Masaya,Competitive-Adaptive Algorithm-Tuning of Metaheuristics inspired by the Equilibrium Theory: A Case Study,IEEE Congress on Evolutionary Computation (CEC), pp.E-24156, (8), IEEE, 2020.
  14. Horiuchi, Motoki and Nakata, Masaya,Self-adaptation of XCS learning parameters based on Learning theory,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.342-349, 2020.
  15. Nakata, Masaya and Browne, Will N,How XCS Can Prevent Misdistinguishing Rule Accuracy: A Preliminary Study,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion, pp.183-184, July 2019.
  16. Takadama, Keiki and Yamazaki, Daichi and Nakata, Masaya and Sato, Hiroyuki,Complex-Valued-based Learning Classifier System for POMDP Environments,IEEE Congress on Evolutionary Computation (CEC), pp.1852-1859, June 2019.
  17. Nakata, Masaya and Browne, Will and Hamagami, Tomoki,Theoretical adaptation of multiple rule-generation in XCS,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.482-489, ACM, July 2018.
  18. Chiba, Kazuhisa and Umeda, Yuhei and Hamada, Naoki and Watanabe, Shinya and Nakata, Masaya and Yasue, Kanako and Suzuki, Koji and Atobe, Takashi and Kuchiishi, Shigeru and Nakatakita, Kazuyuki and Ito, Ken, Determination of Temporal and Spatial Origination of Transonic Buffet via Unsteady Data Mining, Proceedings of AIAA Aerospace Sciences Meeting, 2018-0036, AIAA, January 2018.
  19. Sasaki, Hayato and Nakata, Masaya and Hamatsu, Fumiya and Hamagami, Tomoki,Effect of parameter sharing for multimodal deep autoencoders,Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp.1966-1971, IEEE, October 2017.
  20. Chiba, Kazuhisa and Watanabe, Shinya and Nakata, Masaya and Umeda, Yuhei and Hamada, Naoki and Yasue, Kanako and Suzuki, Koji and Atobe, Takashi and Kuchiishi, Shigeru and Nakatakita, Kazuyuki and Ito, Ken, An Attempt for Detecting Transonic Buffet Signature via Unsteady-Data Mining, Proceedings of the International Conference on Data Mining (DMIN'17), pp.28--33, CSREA, July 2017.
  21. Nakata, Masaya and Browne, Will N and Hamagami, Tomoki and Takadama, Keiki,Theoretical XCS parameter settings of learning accurate classifiers,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.473-480, ACM, July 2017.
  22. Nakata, Masaya and Chiba, Kazuhisa,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.
  23. hiba, Kazuhisa and Watanabe, Shinya and Nakata, Masaya and Umeda, Yuhei and Hamada, Naoki and Yasue, Kanako and Suzuki, Koji and Atobe, Takashi and Kuchiishi, Shigeru and Nakatakita, Kazuyuki and Ito, Ken, An Attempt for Detecting Transonic Buffet Signature via Unsteady-Data Mining, Proceedings of the International Conference on Data Mining (DMIN'17), pp.28--33, CSREA, July 2017.
  24. Tatsumi, Takato and Komine, Takahiro and Nakata, Masaya and Sato, Hiroyuki and Kovacs, Tim and Takadama, Keiki, 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.
  25. Umenai, Yuta and Uwano, Fumito and Tajima, Yusuke and Nakata, Masaya and Sato, Hiroyuki and Takadama, Keiki,A Modified Cuckoo Search Algorithm for Dynamic Optimization Problems,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.1754-1764, IEEE, July 2016.
  26. Kovacs, Tim and Rawles, Simon and Bull, Larry and Nakata, Masaya and Takadama, Keiki,XCS-DH: Minimal Default Hierarchies in XCS,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.4747-4754, IEEE, July 2016.
  27. Matsumoto, Kazuma and Saito, Rei and Tajima, Yusuke and Nakata, Masaya and Sato, Hiroyuki and Kovacs, Tim and Takadama, Keiki,Learning Classifier System with Deep Auto-encoder,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.4739-4746, IEEE, July 2016.
  28. Saito, Rei and Nakata, Masaya and Sato, Hiroyuki and Kovacs, Tim and Takadama, Keiki,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.
  29. Uwano, Fumito and Tatebe, Naoki and Nakata, Masaya and Takadama, Keiki 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.
  30. Murata, Akinori and Nakata, Masaya and Sato, Hiroyuki and Kovacs, Tim and Takadama, Keiki, 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.
  31. Nakata, Masaya and Pier Luca Lanzi and Kovacs, Tim and Browne, Will and 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.
  32. Takadama, Keiki and Nakata, Masaya,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.
  33. Sato, Minato and Usui, Kotaro and Nakata, Masaya and Takadama, Keiki,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.
  34. Nakata, Masaya and Kovacs, Tim and Takadama, Keiki, 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.
  35. Nakata, Masaya and Kovacs, Tim and Takadama, Keiki, A Modified XCS Classifier System for Sequence Labelling,Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp.565-572, ACM, July 2014.
  36. Nakata, Masaya and Pier Luca Lanzi and Kovacs, Tim and Takadama, Keiki, 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.
  37. Usui, Kotaro and Nakata, Masaya and Takadama, Keiki,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.
  38. Komine, Takahiro and Nakata, Masaya and Takadama, Keiki,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.
  39. Tajima, Yusuke and Nakata, Masaya and Takadama, Keiki,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.
  40. Tajima, Yusuke and Nakata, Masaya and Harada, Tomohiro and Sato, Keiji and Takadama, Keiki,Sleep stage estimation using synthesized data of heart rate and body movement,Proceedings of AAAI Spring Symposium, pp.63-68, AAAI, March 2014.
  41. Nakata, Masaya and Pier Luca Lanzi and Takadama, Keiki,Selection Strategy for XCS with Adaptive Action Mapping,Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp.1085-1092, ACM, July 2013.
  42. Nakata, Masaya and Pier Luca Lanzi and Takadama, Keiki,Simple Compact Genetic Algorithm for XCS,Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp.1718-1723, IEEE, June 2013.
  43. Nakata, Masaya and Pier Luca Lanzi and Takadama, Keiki,XCS with adaptive action mapping,Proceedings of 9th International Conference on Simulated Evolution and Learning, pp.138-147, Springer, December 2012.
  44. Nakata, Masaya and Pier Luca Lanzi and Takadama, Keiki,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.
  45. Nakata, Masaya and Sato, Fumiaki and Takadama, Keiki,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.