Masaya Nakata
Title
Cited by
Cited by
Year
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
252017
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
152014
XCS with adaptive action mapping
M Nakata, PL Lanzi, K Takadama
Proceedings of the 9th international conference on Simulated Evolution and …, 2012
142012
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
102015
Simple compact genetic algorithm for XCS
M Nakata, PL Lanzi, K Takadama
2013 IEEE Congress on Evolutionary Computation, 1718-1723, 2013
102013
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
102011
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
92018
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
92016
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
92012
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
72016
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
72015
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
72015
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
62020
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
62018
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
62016
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
52015
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
52015
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
52014
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
52013
Learning Optimality Theory for Accuracy-Based Learning Classifier Systems
M Nakata, WN Browne
IEEE Transactions on Evolutionary Computation 25 (1), 61-74, 2020
42020
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Articles 1–20