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
172017
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
132014
XCS with adaptive action mapping
M Nakata, PL Lanzi, K Takadama
Proceedings of the 9th international conference on Simulated Evolution and …, 2012
132012
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
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
92015
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
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
72018
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
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
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
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
62015
A learning classifier system that adapts accuracy criterion
T Tatsumi, T Komine, M Nakata, H Sato, K Takadama
Transaction of the Japanese Society for Evolutionary Computation 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
Design strategy generation for a sounding hybrid rocket via evolutionary rule-based data mining system
M Nakata, K Chiba
Intelligent and Evolutionary Systems, 305-318, 2017
42017
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
42015
How XCS can prevent misdistinguishing rule accuracy: a preliminary study
M Nakata, WN Browne
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019
32019
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
32018
Optimization of aircraft landing route and order: An approach of hierarchical evolutionary computation
A Murata, M Nakata, H Sato, T Kovacs, K Takadama
Proceedings of the 9th EAI International Conference on Bio-inspired …, 2016
32016
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