| 2026 | GECCO | Classifier-Based Feasibility Estimation for Expensive Constrained Multiobjective Optimization. | Yuma Horaguchi, Hiroki Shiraishi, Masaya Nakata |
| 2026 | PPSN | An Easy-to-Use Extension of Surrogate-Assisted Multi-Objective Evolutionary Algorithms for Expensive Robust Optimization. | Takuro Tanaka, Yuma Horaguchi, Yuma Yamaguchi, Kei Nishihara, Masaya Nakata |
| 2025 | GECCO | Evolutionary Multiobjective Optimization Assisted by Scalarization Function Approximation for High-Dimensional Expensive Problems (HOP GECCO'25). | Yuma Horaguchi, Kei Nishihara, Masaya Nakata |
| 2025 | GECCO | High-Dimensional Expensive Multiobjective Optimization Using a Surrogate-Assisted Multifactorial Evolutionary Algorithm. | Yuma Horaguchi, Masaya Nakata |
| 2025 | GECCO | Evolutionary Co-Optimization of Rule Shape and Fuzziness in Rule-Based Machine Learning. | Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama, Keiki Takadama, Hisao Ishibuchi, Masaya Nakata |
| 2025 | GECCO | Evidential Fuzzy Rule-Based Machine Learning to Quantify Classification Uncertainty. | Hiroki Shiraishi, Hisao Ishibuchi, Masaya Nakata |
| 2025 | IJCAI | X-KAN: Optimizing Local Kolmogorov-Arnold Networks via Evolutionary Rule-Based Machine Learning. | Hiroki Shiraishi, Hisao Ishibuchi, Masaya Nakata |
| 2025 | IJCCI | Sustainable Performance Improvement of Surrogate-Assisted Evolutionary Algorithms Using Tabu Search. | Kei Nishihara, Masaya Nakata, Shinya Watanabe |
| 2024 | CEC | A Dual Surrogate-Based Evolutionary Algorithm for High-Dimensional Expensive Multiobjective Optimization Problems. | Yuma Horaguchi, Masaya Nakata |
| 2024 | CEC | A Random Forest-Assisted Local Search for Expensive Permutation-based Combinatorial Optimization Problems. | Takashi Ikeguchi, Shun Sudo, Yuji Koguma, Masaya Nakata |
| 2024 | CEC | Oversampling-Guided Search for Evolutionary Multiobjective Optimization. | Norihiro Kimoto, Yuma Horaguchi, Masaya Nakata |
| 2024 | PPSN | A Surrogate-Assisted Partial Optimization for Expensive Constrained Optimization Problems. | Kei Nishihara, Masaya Nakata |
| 2024 | PPSN | A Variable-Length Fuzzy Set Representation for Learning Fuzzy-Classifier Systems. | Hiroki Shiraishi, Rongguang Ye, Hisao Ishibuchi, Masaya Nakata |
| 2023 | GECCO | Utilizing the Expected Gradient in Surrogate-assisted Evolutionary Algorithms. | Kei Nishihara, Masaya Nakata |
| 2021 | CEC | Comparison of Adaptive Differential Evolution Algorithms on the MOEA/D-DE Framework. | Kei Nishihara, Masaya Nakata |
| 2021 | GECCO | Convergence analysis of rule-generality on the XCS classifier system. | Yoshiki Nakamura, Motoki Horiuchi, Masaya Nakata |
| 2021 | GECCO | Learning classifier systems: from principles to modern systems. | Anthony Stein, Masaya Nakata |
| 2020 | CEC | Competitive-Adaptive Algorithm-Tuning of Metaheuristics inspired by the Equilibrium Theory: A Case Study. | Kei Nishihara, Masaya Nakata |
| 2020 | CEC | MOEA/D-S | Takumi Sonoda, Masaya Nakata |
| 2020 | GECCO | Self-adaptation of XCS learning parameters based on learning theory. | Motoki Horiuchi, Masaya Nakata |
| 2020 | GECCO | An overview of LCS research from IWLCS 2019 to 2020. | David Ptzel, Anthony Stein, Masaya Nakata |
| 2020 | GECCO | Learning classifier systems: from principles to modern systems. | Anthony Stein, Masaya Nakata |
| 2019 | CEC | Complex-Valued-based Learning Classifier System for POMDP Environments. | Keiki Takadama, Daichi Yamazaki, Masaya Nakata, Hiroyuki Sato |
| 2019 | GECCO | How XCS can prevent misdistinguishing rule accuracy: a preliminary study. | Masaya Nakata, Will Neil Browne |
| 2018 | GECCO | Theoretical adaptation of multiple rule-generation in XCS. | Masaya Nakata, Will N. Browne, Tomoki Hamagami |
| 2018 | SMC | Investigation about Control of False Positive Rate for Automatic Sperm Detection in Assisted Reproductive Technology. | Hayato Sasaki, Masaya Nakata, Mizuki Yamamoto, Teppei Takeshima, Yasushi Yumura, Tomoki Hamagami |
| 2017 | GECCO | Theoretical XCS parameter settings of learning accurate classifiers. | Masaya Nakata, Will N. Browne, Tomoki Hamagami, Keiki Takadama |
| 2017 | ICCS | From Extraction to Generation of Design Information -Paradigm Shift in Data Mining via Evolutionary Learning Classifier System. | Kazuhisa Chiba, Masaya Nakata |
| 2017 | SMC | Effect of parameter sharing for multimodal deep autoencoders. | Hayato Sasaki, Masaya Nakata, Fumiya Hamatsu, Tomoki Hamagami |
| 2016 | CEC | XCS-DH: Minimal default hierarchies in XCS. | Tim Kovacs, Simon Rawles, Larry Bull, Masaya Nakata, Keiki Takadama |
| 2016 | CEC | Learning classifier system with deep autoencoder. | Kazuma Matsumoto, Yusuke Tajima, Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2016 | CEC | A modified cuckoo search algorithm for dynamic optimization problems. | Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Keiki Takadama |
| 2016 | GECCO | International Workshop on Evolutionary Rule-Based Machine Learning Workshop (IWERML) Welcome & Organization. | Karthik Kuber, Masaya Nakata, Kamran Shafi |
| 2016 | GECCO | Variance-based Learning Classifier System without Convergence of Reward Estimation. | Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2016 | HCI | Preventing Incorrect Opinion Sharing with Weighted Relationship Among Agents. | Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2015 | CEC | How should Learning Classifier Systems cover a state-action space? | Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Will Neil Browne, Keiki Takadama |
| 2015 | CEC | Detecting shoplifting from customer behavior data by extended XCS-SL: Towards feature extraction on class-imbalanced sequence data. | Minato Sato, Kotaro Usui, Masaya Nakata, Keiki Takadama |
| 2015 | CEC | Extracting both generalized and specialized knowledge by XCS using Attribute Tracking and Feedback. | Keiki Takadama, Masaya Nakata |
| 2014 | GECCO | A modified XCS classifier system for sequence labeling. | Masaya Nakata, Tim Kovacs, Keiki Takadama |
| 2014 | GECCO | Complete action map or best action map in accuracy-based reinforcement learning classifier systems. | Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Keiki Takadama |
| 2014 | PPSN | Messy Coding in the XCS Classifier System for Sequence Labeling. | Masaya Nakata, Tim Kovacs, Keiki Takadama |
| 2013 | CEC | Simple compact genetic algorithm for XCS. | Masaya Nakata, Pier Luca Lanzi, Keiki Takadama |
| 2013 | GECCO | Selection strategy for XCS with adaptive action mapping. | Masaya Nakata, Pier Luca Lanzi, Keiki Takadama |
| 2012 | PPSN | Enhancing Learning Capabilities by XCS with Best Action Mapping. | Masaya Nakata, Pier Luca Lanzi, Keiki Takadama |