フォロー
Masaya Nakata
Masaya Nakata
確認したメール アドレス: ynu.ac.jp
タイトル
引用先
引用先
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
402022
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
372017
Particle swarm optimization of silicon photonic crystal waveguide transition
R Shiratori, M Nakata, K Hayashi, T Baba
Optics letters 46 (8), 1904-1907, 2021
342021
Learning optimality theory for accuracy-based learning classifier systems
M Nakata, WN Browne
IEEE Transactions on Evolutionary Computation 25 (1), 61-74, 2020
212020
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
172014
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
142020
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
142018
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
122018
Learning classifier systems: from principles to modern systems
A Stein, M Nakata
Proceedings of the genetic and evolutionary computation conference companion …, 2021
112021
MOEA/D-S3: MOEA/D using SVM-based Surrogates adjusted to Subproblems for Many objective optimization
T Sonoda, M Nakata
2020 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2020
112020
XCS with adaptive action mapping
M Nakata, PL Lanzi, K Takadama
Asia-Pacific Conference on Simulated Evolution and Learning, 138-147, 2012
112012
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
102016
Rule reduction by selection strategy in XCS with adaptive action map
M Nakata, PL Lanzi, K Takadama
Evolutionary Intelligence 8, 71-87, 2015
102015
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
102015
Simple compact genetic algorithm for XCS
M Nakata, PL Lanzi, K Takadama
2013 IEEE Congress on Evolutionary Computation, 1718-1723, 2013
102013
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
102012
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
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
82016
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
72020
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
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