| 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 | 30 | 2017 |
| 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 | 18 | 2012 |
| 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 | 16 | 2014 |
| Learning optimality theory for accuracy-based learning classifier systems M Nakata, WN Browne IEEE Transactions on Evolutionary Computation 25 (1), 61-74, 2020 | 14 | 2020 |
| Particle swarm optimization of silicon photonic crystal waveguide transition R Shiratori, M Nakata, K Hayashi, T Baba Optics letters 46 (8), 1904-1907, 2021 | 11 | 2021 |
| 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 | 11 | 2020 |
| 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 | 11 | 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 | 10 | 2016 |
| Rule reduction by selection strategy in XCS with adaptive action map M Nakata, PL Lanzi, K Takadama Evolutionary Intelligence 8 (2), 71-87, 2015 | 10 | 2015 |
| 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 |
| 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 | 9 | 2018 |
| 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 |
| Simple compact genetic algorithm for XCS M Nakata, PL Lanzi, K Takadama 2013 IEEE Congress on Evolutionary Computation, 1718-1723, 2013 | 9 | 2013 |
| 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 |
| Learning classifier systems: from principles to modern systems A Stein, M Nakata Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 6 | 2021 |
| 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 | 6 | 2020 |
| 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 |
| 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 | 6 | 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 | 6 | 2013 |
Tim KovacsUniversity of Bristol確認したメール アドレス: bristol.ac.uk
