Zobrazeno 1 - 10
of 199
pro vyhledávání: '"Murata, Junichi"'
Unit commitment and load dispatch problems are important and complex problems in power system operations that have being traditionally solved separately. In this paper, both problems are solved together without approximations or simplifications. In f
Externí odkaz:
http://arxiv.org/abs/1903.09304
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, Volume 28, Issue 8, 1759-1773, 2017
Learning algorithms are being increasingly adopted in various applications. However, further expansion will require methods that work more automatically. To enable this level of automation, a more powerful solution representation is needed. However,
Externí odkaz:
http://arxiv.org/abs/1902.06703
Autor:
Vargas, Danilo Vasconcellos, Murata, Junichi, Takano, Hirotaka, Delbem, Alexandre Claudio Botazzo
Publikováno v:
Evolutionary computation 23 (1), 1-36, 2015
Structured evolutionary algorithms have been investigated for some time. However, they have been under-explored specially in the field of multi-objective optimization. Despite their good results, the use of complex dynamics and structures make their
Externí odkaz:
http://arxiv.org/abs/1901.00266
Publikováno v:
Proc. of SICE Annual Conference 2013
When applied to high-dimensional datasets, feature selection algorithms might still leave dozens of irrelevant variables in the dataset. Therefore, even after feature selection has been applied, classifiers must be prepared to the presence of irrelev
Externí odkaz:
http://arxiv.org/abs/1811.08214
Publikováno v:
Proceedings of the 15th annual conference on Genetic and evolutionary computation (GECCO 2013)
Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problems unsolved. The hurdle is that fitness and niching pressures are diffi
Externí odkaz:
http://arxiv.org/abs/1811.08226
Publikováno v:
Evolutionary Intelligence 6 (2), 57-72 (2013)
Learning classifier systems (LCSs) are evolutionary machine learning algorithms, flexible enough to be applied to reinforcement, supervised and unsupervised learning problems with good performance. Recently, self organizing classifiers were proposed
Externí odkaz:
http://arxiv.org/abs/1811.08225
Publikováno v:
2015 IEEE Congress on Evolutionary Computation (CEC)
In the real world, the environment is constantly changing with the input variables under the effect of noise. However, few algorithms were shown to be able to work under those circumstances. Here, Novelty-Organizing Team of Classifiers (NOTC) is appl
Externí odkaz:
http://arxiv.org/abs/1809.07098
Publikováno v:
In IFAC PapersOnLine 2022 55(9):401-406
Publikováno v:
In IFAC PapersOnLine 2018 51(28):486-491
Publikováno v:
In Applied Thermal Engineering 5 March 2017 114:1424-1432