| 2026 | GECCO | Classifier-Based Feasibility Estimation for Expensive Constrained Multiobjective Optimization. | Yuma Horaguchi, Hiroki Shiraishi, Masaya Nakata |
| 2026 | GECCO | Recent Advances in Evolutionary Rule-Based Machine Learning (2024 to 2026). | Connor Schnberner, Hiroki Shiraishi, Fumito Uwano, Michael Heider |
| 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 |
| 2024 | CEC | Prototype Generation with the sUpervised Classifier System on kNN Matching. | Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama |
| 2024 | GECCO | A Survey on Learning Classifier Systems from 2022 to 2024. | Abubakar Siddique, Michael Heider, Muhammad Iqbal, Hiroki Shiraishi |
| 2024 | GECCO | Generating High-Dimensional Prototypes with a Classifier System by Evolving in Latent Space. | Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama |
| 2024 | PPSN | A Variable-Length Fuzzy Set Representation for Learning Fuzzy-Classifier Systems. | Hiroki Shiraishi, Rongguang Ye, Hisao Ishibuchi, Masaya Nakata |
| 2023 | GECCO | Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems. | Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama |
| 2023 | GECCO | Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS. | Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama |
| 2022 | CEC | Beta Distribution based XCS Classifier System. | Hiroki Shiraishi, Yohei Havamizu, Hiroyuki Sato, Keiki Takadama |
| 2022 | CEC | XCSR with VAE using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space. | Naoya Yatsu, Hiroki Shiraishi, Hiroyuki Sato, Keiki Takadama |
| 2022 | GECCO | Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputs. | Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama |
| 2022 | GECCO | Can the same rule representation change its matching area?: enhancing representation in XCS for continuous space by probability distribution in multiple dimension. | Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama |
| 2021 | CEC | Increasing Accuracy and Interpretability of High-Dimensional Rules for Learning Classifier System. | Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, Keiki Takadama |
| 2021 | GECCO | Misclassification detection based on conditional VAE for rule evolution in learning classifier system. | Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, Keiki Takadama |