| 2020 | CEC | Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS. | Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama |
| 2019 | CEC | Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models. | Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama |
| 2019 | CEC | Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments. | Takato Tatsumi, Keiki Takadama |
| 2019 | GECCO | XCS-CR for handling input, output, and reward noise. | Takato Tatsumi, Keiki Takadama |
| 2018 | GECCO | XCSR based on compressed input by deep neural network for high dimensional data. | Kazuma Matsumoto, Ryo Takano, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2018 | GECCO | XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise. | Takato Tatsumi, Tim Kovacs, Keiki Takadama |
| 2018 | GECCO | Classifier generalization for comprehensive classifiers subsumption in XCS. | Caili Zhang, Takato Tatsumi, Hiyoyuki Sato, Tim Kovacs, Keiki Takadama |
| 2017 | CEC | Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution. | Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2017 | GECCO | Automatic adjustment of selection pressure based on range of reward in learning classifier system. | Takato Tatsumi, Hiroyuki Sato, Keiki Takadama |
| 2016 | GECCO | Variance-based Learning Classifier System without Convergence of Reward Estimation. | Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2015 | CEC | Handling different level of unstable reward environment through an estimation of reward distribution in XCS. | Takato Tatsumi, Takahiro Komine, Hiroyuki Sato, Keiki Takadama |