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 | 37 | 2017 |

Multiple classifiers-assisted evolutionary algorithm based on decomposition for high-dimensional multiobjective problems T Sonoda, M Nakata IEEE Transactions on Evolutionary Computation 26 (6), 1581-1595, 2022 | 35 | 2022 |

Particle swarm optimization of silicon photonic crystal waveguide transition R Shiratori, M Nakata, K Hayashi, T Baba Optics letters 46 (8), 1904-1907, 2021 | 33 | 2021 |

Learning optimality theory for accuracy-based learning classifier systems M Nakata, WN Browne IEEE Transactions on Evolutionary Computation 25 (1), 61-74, 2020 | 21 | 2020 |

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 | 17 | 2014 |

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 | 14 | 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 | 14 | 2018 |

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 | 12 | 2018 |

Learning classifier systems: from principles to modern systems A Stein, M Nakata Proceedings of the genetic and evolutionary computation conference companion …, 2021 | 11 | 2021 |

XCS with adaptive action mapping M Nakata, PL Lanzi, K Takadama Asia-Pacific Conference on Simulated Evolution and Learning, 138-147, 2012 | 11 | 2012 |

MOEA/D-S^{3}: MOEA/D using SVM-based Surrogates adjusted to Subproblems for Many objective optimizationT Sonoda, M Nakata 2020 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2020 | 10 | 2020 |

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 | 10 | 2016 |

Rule reduction by selection strategy in XCS with adaptive action map M Nakata, PL Lanzi, K Takadama Evolutionary Intelligence 8, 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 |

Enhancing learning capabilities by XCS with best action mapping M Nakata, PL Lanzi, K Takadama Parallel Problem Solving from Nature-PPSN XII: 12th International Conference …, 2012 | 10 | 2012 |

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 |

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 | 9 | 2015 |

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 | 8 | 2016 |

Self-adaptation of XCS learning parameters based on learning theory M Horiuchi, M Nakata Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 342-349, 2020 | 7 | 2020 |

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 |