Theoretical XCS parameter settings of learning accurate classifiers M Nakata, W Browne, T Hamagami, K Takadama Proceedings of the Genetic and Evolutionary Computation Conference, 473-480, 2017 | 25 | 2017 |

A modified XCS classifier system for sequence labeling M Nakata, T Kovacs, K Takadama Proceedings of the 2014 Annual Conference on Genetic and Evolutionary …, 2014 | 15 | 2014 |

XCS with adaptive action mapping M Nakata, PL Lanzi, K Takadama Proceedings of the 9th international conference on Simulated Evolution and …, 2012 | 14 | 2012 |

Rule reduction by selection strategy in XCS with adaptive action map M Nakata, PL Lanzi, K Takadama Evolutionary Intelligence 8 (2-3), 71-87, 2015 | 10 | 2015 |

Simple compact genetic algorithm for XCS M Nakata, PL Lanzi, K Takadama 2013 IEEE Congress on Evolutionary Computation, 1718-1723, 2013 | 10 | 2013 |

Towards generalization by identification-based XCS in multi-steps problem M Nakata, F Sato, K Takadama 2011 Third World Congress on Nature and Biologically Inspired Computing, 389-394, 2011 | 10 | 2011 |

Theoretical adaptation of multiple rule-generation in XCS M Nakata, W Browne, T Hamagami Proceedings of the Genetic and Evolutionary Computation Conference, 482-489, 2018 | 9 | 2018 |

A modified cuckoo search algorithm for dynamic optimization problems Y Umenai, F Uwano, Y Tajima, M Nakata, H Sato, K Takadama 2016 IEEE Congress on evolutionary computation (CEC), 1757-1764, 2016 | 9 | 2016 |

Enhancing learning capabilities by XCS with best action mapping M Nakata, PL Lanzi, K Takadama International Conference on Parallel Problem Solving from Nature, 256-265, 2012 | 9 | 2012 |

Learning classifier system with deep autoencoder K Matsumoto, Y Tajima, R Saito, M Nakata, H Sato, T Kovacs, ... 2016 IEEE Congress on Evolutionary Computation (CEC), 4739-4746, 2016 | 7 | 2016 |

Extracting both generalized and specialized knowledge by xcs using attribute tracking and feedback K Takadama, M Nakata 2015 IEEE Congress on Evolutionary Computation (CEC), 3034-3041, 2015 | 7 | 2015 |

How should learning classifier systems cover a state-action space? M Nakata, PL Lanzi, T Kovacs, WN Browne, K Takadama 2015 IEEE Congress on Evolutionary Computation (CEC), 3012-3019, 2015 | 7 | 2015 |

An overview of LCS research from IWLCS 2019 to 2020 D Pätzel, A Stein, M Nakata Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 6 | 2020 |

Multi-agent cooperation based on reinforcement learning with internal reward in maze problem F Uwano, N Tatebe, Y Tajima, M Nakata, T Kovacs, K Takadama SICE Journal of Control, Measurement, and System Integration 11 (4), 321-330, 2018 | 6 | 2018 |

Variance-based learning classifier system without convergence of reward estimation T Tatsumi, T Komine, M Nakata, H Sato, T Kovacs, K Takadama Proceedings of the 2016 on Genetic and Evolutionary Computation Conference …, 2016 | 6 | 2016 |

XCS-SL: a rule-based genetic learning system for sequence labeling M Nakata, T Kovacs, K Takadama Evolutionary Intelligence 8 (2-3), 133-148, 2015 | 5 | 2015 |

A learning classifier system that adapts accuracy criterion T TATSUMI, T KOMINE, M NAKATA, H SATO, K TAKADAMA 進化計算学会論文誌 (Web) 6 (2), 90-103, 2015 | 5 | 2015 |

Complete action map or best action map in accuracy-based reinforcement learning classifier systems M Nakata, PL Lanzi, T Kovacs, K Takadama Proceedings of the 2014 Annual Conference on Genetic and Evolutionary …, 2014 | 5 | 2014 |

Selection strategy for XCS with adaptive action mapping M Nakata, PL Lanzi, K Takadama Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013 | 5 | 2013 |

Learning Optimality Theory for Accuracy-Based Learning Classifier Systems M Nakata, WN Browne IEEE Transactions on Evolutionary Computation 25 (1), 61-74, 2020 | 4 | 2020 |