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Masaya Nakata
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2020 – today
- 2024
- [j12]Yuma Horaguchi
, Kei Nishihara
Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems. Swarm Evol. Comput. 86: 101516 (2024) - [c49]Yuma Horaguchi
A Dual Surrogate-Based Evolutionary Algorithm for High-Dimensional Expensive Multiobjective Optimization Problems. CEC 2024: 1-8 - [c48]Takashi Ikeguchi, Shun Sudo, Yuji Koguma, Masaya Nakata:
A Random Forest-Assisted Local Search for Expensive Permutation-based Combinatorial Optimization Problems. CEC 2024: 1-8 - [c47]Norihiro Kimoto, Yuma Horaguchi
Oversampling-Guided Search for Evolutionary Multiobjective Optimization. CEC 2024: 1-8 - [c46]Hiroki Shiraishi
A Variable-Length Fuzzy Set Representation for Learning Fuzzy-Classifier Systems. PPSN (3) 2024: 386-402 - [c45]Kei Nishihara
A Surrogate-Assisted Partial Optimization for Expensive Constrained Optimization Problems. PPSN (2) 2024: 391-407 - 2023
- [c44]Kei Nishihara
Utilizing the Expected Gradient in Surrogate-assisted Evolutionary Algorithms. GECCO Companion 2023: 447-450 - [c43]Yuma Horaguchi
High-Dimensional Expensive Optimization by Classification-based Multiobjective Evolutionary Algorithm with Dimensionality Reduction. SICE 2023: 1535-1542 - 2022
- [j11]Rui Sugawara
Theoretical Analysis of Accuracy-Based Fitness on Learning Classifier Systems. IEEE Access 10: 64862-64872 (2022) - [j10]Yusuke Hiruta
Automatic Construction of Loading Algorithms With Interactive Genetic Programming. IEEE Access 10: 125167-125180 (2022) - [j9]Takumi Sonoda, Masaya Nakata
Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multiobjective Problems. IEEE Trans. Evol. Comput. 26(6): 1581-1595 (2022) - [c42]Kei Nishihara, Masaya Nakata:
Surrogate-assisted Differential Evolution with Adaptation of Training Data Selection Criterion. SSCI 2022: 1675-1682 - 2021
- [j8]Masaya Nakata
Learning Optimality Theory for Accuracy-Based Learning Classifier Systems. IEEE Trans. Evol. Comput. 25(1): 61-74 (2021) - [c41]Kei Nishihara, Masaya Nakata:
Comparison of Adaptive Differential Evolution Algorithms on the MOEA/D-DE Framework. CEC 2021: 161-168 - [c40]Yoshiki Nakamura, Motoki Horiuchi, Masaya Nakata:
Convergence analysis of rule-generality on the XCS classifier system. GECCO 2021: 332-339 - [c39]Anthony Stein, Masaya Nakata:
Learning classifier systems: from principles to modern systems. GECCO Companion 2021: 498-527 - [c38]Motoki Horiuchi, Masaya Nakata:
Extended Learning Optimality Theory for the XCS classifier system on Multiple Reward Scheme. SSCI 2021: 1-8 - 2020
- [c37]Kei Nishihara, Masaya Nakata:
Competitive-Adaptive Algorithm-Tuning of Metaheuristics inspired by the Equilibrium Theory: A Case Study. CEC 2020: 1-8 - [c36]Takumi Sonoda, Masaya Nakata:
MOEA/D-S3: MOEA/D using SVM-based Surrogates adjusted to Subproblems for Many objective optimization. CEC 2020: 1-8 - [c35]Motoki Horiuchi, Masaya Nakata:
Self-adaptation of XCS learning parameters based on learning theory. GECCO 2020: 342-349 - [c34]Anthony Stein
Learning classifier systems: from principles to modern systems. GECCO Companion 2020: 561-589 - [c33]David Pätzel, Anthony Stein
An overview of LCS research from IWLCS 2019 to 2020. GECCO Companion 2020: 1782-1788
2010 – 2019
- 2019
- [c32]Keiki Takadama, Daichi Yamazaki, Masaya Nakata, Hiroyuki Sato:
Complex-Valued-based Learning Classifier System for POMDP Environments. CEC 2019: 1852-1859 - [c31]Masaya Nakata, Will Neil Browne
How XCS can prevent misdistinguishing rule accuracy: a preliminary study. GECCO (Companion) 2019: 183-184 - 2018
- [c30]Masaya Nakata, Will N. Browne
Theoretical adaptation of multiple rule-generation in XCS. GECCO 2018: 482-489 - [c29]Hayato Sasaki, Masaya Nakata, Mizuki Yamamoto, Teppei Takeshima, Yasushi Yumura, Tomoki Hamagami:
Investigation about Control of False Positive Rate for Automatic Sperm Detection in Assisted Reproductive Technology. SMC 2018: 1964-1969 - 2017
- [j7]Masaya Nakata, Tomoki Hamagami:
An Analysis of Rule Deletion Scheme in XCS on Reinforcement Learning Problem. J. Adv. Comput. Intell. Intell. Informatics 21(5): 876-884 (2017) - [j6]Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama:
Approach to Clustering with Variance-Based XCS. J. Adv. Comput. Intell. Intell. Informatics 21(5): 885-894 (2017) - [c28]Masaya Nakata, Will N. Browne
Theoretical XCS parameter settings of learning accurate classifiers. GECCO 2017: 473-480 - [c27]Kazuhisa Chiba, Masaya Nakata:
From Extraction to Generation of Design Information -Paradigm Shift in Data Mining via Evolutionary Learning Classifier System. ICCS 2017: 1662-1671 - [c26]Hayato Sasaki, Masaya Nakata, Fumiya Hamatsu, Tomoki Hamagami:
Effect of parameter sharing for multimodal deep autoencoders. SMC 2017: 1966-1971 - 2016
- [j5]Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs:
Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach. EAI Endorsed Trans. Collab. Comput. 2(8): e2 (2016) - [j4]Akinori Murata, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation. EAI Endorsed Trans. Self Adapt. Syst. 2(6): e5 (2016) - [c25]Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Keiki Takadama:
A modified cuckoo search algorithm for dynamic optimization problems. CEC 2016: 1757-1764 - [c24]Kazuma Matsumoto, Yusuke Tajima, Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Learning classifier system with deep autoencoder. CEC 2016: 4739-4746 - [c23]Tim Kovacs, Simon Rawles, Larry Bull, Masaya Nakata, Keiki Takadama:
XCS-DH: Minimal default hierarchies in XCS. CEC 2016: 4747-4754 - [c22]Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Variance-based Learning Classifier System without Convergence of Reward Estimation. GECCO (Companion) 2016: 67-68 - [c21]Karthik Kuber, Masaya Nakata, Kamran Shafi:
International Workshop on Evolutionary Rule-Based Machine Learning Workshop (IWERML) Welcome & Organization. GECCO (Companion) 2016: 1409-1410 - [c20]Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Preventing Incorrect Opinion Sharing with Weighted Relationship Among Agents. HCI (5) 2016: 50-62 - [c19]Masaya Nakata, Kazuhisa Chiba:
Proceedings in Adaptation, Learning and Optimization. IES 2016: 305-318 - [c18]Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama, Hiroyuki Sato, Tim Kovacs:
Extracting Different Abstracted Level Rule with Variance-Based LCS. SCIS&ISIS 2016: 160-165 - 2015
- [j3]Masaya Nakata, Pier Luca Lanzi
Rule reduction by selection strategy in XCS with adaptive action map. Evol. Intell. 8(2-3): 71-87 (2015) - [j2]Masaya Nakata, Tim Kovacs, Keiki Takadama:
XCS-SL: a rule-based genetic learning system for sequence labeling. Evol. Intell. 8(2-3): 133-148 (2015) - [j1]Yusuke Tajima, Masaya Nakata, Hiroyasu Matsushima
Evolutionary Algorithm for Uncertain Evaluation Function. New Math. Nat. Comput. 11(2): 201-215 (2015) - [c17]Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs:
Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach. BICT 2015: 332-339 - [c16]Akinori Murata, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation. BICT 2015: 340-347 - [c15]Minato Sato, Kotaro Usui, Masaya Nakata, Keiki Takadama:
Detecting shoplifting from customer behavior data by extended XCS-SL: Towards feature extraction on class-imbalanced sequence data. CEC 2015: 2981-2988 - [c14]Masaya Nakata, Pier Luca Lanzi
How should Learning Classifier Systems cover a state-action space? CEC 2015: 3012-3019 - [c13]Keiki Takadama, Masaya Nakata:
Extracting both generalized and specialized knowledge by XCS using Attribute Tracking and Feedback. CEC 2015: 3034-3041 - 2014
- [c12]Yusuke Tajima, Masaya Nakata, Tomohiro Harada, Keiji Sato, Keiki Takadama:
Sleep Stage Estimation Using Synthesized Data of Heart Rate and Body Movement. AAAI Spring Symposia 2014 - [c11]Masaya Nakata, Pier Luca Lanzi
Complete action map or best action map in accuracy-based reinforcement learning classifier systems. GECCO 2014: 557-564 - [c10]Masaya Nakata, Tim Kovacs, Keiki Takadama:
A modified XCS classifier system for sequence labeling. GECCO 2014: 565-572 - [c9]Yusuke Tajima, Masaya Nakata, Keiki Takadama:
Personalized real-time sleep stage remote monitoring system. ISMICT 2014: 1-5 - [c8]Takahiro Komine, Masaya Nakata, Keiki Takadama:
Archives-holding XCS Classifier System: A preliminary study. NaBIC 2014: 53-58 - [c7]Kotaro Usui, Masaya Nakata, Keiki Takadama:
Reusable knowledge by linkage-classifier in Accuracy-based Learning Classifier System. NaBIC 2014: 312-317 - [c6]Masaya Nakata, Tim Kovacs, Keiki Takadama:
Messy Coding in the XCS Classifier System for Sequence Labeling. PPSN 2014: 191-200 - 2013
- [c5]Masaya Nakata, Pier Luca Lanzi
Simple compact genetic algorithm for XCS. IEEE Congress on Evolutionary Computation 2013: 1718-1723 - [c4]Masaya Nakata, Pier Luca Lanzi
Selection strategy for XCS with adaptive action mapping. GECCO 2013: 1085-1092 - 2012
- [c3]Masaya Nakata, Pier Luca Lanzi
Enhancing Learning Capabilities by XCS with Best Action Mapping. PPSN (1) 2012: 256-265 - [c2]Masaya Nakata, Pier Luca Lanzi
XCS with Adaptive Action Mapping. SEAL 2012: 138-147 - 2011
- [c1]Masaya Nakata, Fumiaki Sato, Keiki Takadama:
Towards generalization by identification-based XCS in multi-steps problem. NaBIC 2011: 389-394
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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