Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Lie Meng Pang"'
Publikováno v:
IEEE Access, Vol 8, Pp 163197-163208 (2020)
The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. Over the years, many EMO algorithms have been proposed. The existing EM
Externí odkaz:
https://doaj.org/article/c949d586fd764192a2f458d13da1a8da
Publikováno v:
IEEE Access, Vol 8, Pp 144908-144930 (2020)
A monotone fuzzy rule relabeling (MFRR) algorithm has been introduced previously for tackling the issue of a non-monotone fuzzy rule base in the Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS). In this paper, we further propose a new three-stag
Externí odkaz:
https://doaj.org/article/27266719ae094fc581427110b3d7188c
Publikováno v:
IEEE Access, Vol 8, Pp 190240-190250 (2020)
In the last two decades, the non-dominated sorting genetic algorithm II (NSGA-II) has been the most widely-used evolutionary multi-objective optimization (EMO) algorithm. However, its performance on a wide variety of many-objective test problems has
Externí odkaz:
https://doaj.org/article/aca1338f13cb46e284e0246d2a0f2212
Publikováno v:
Information Sciences. 622:755-770
Publikováno v:
IEEE Transactions on Evolutionary Computation. 26:1609-1616
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031272493
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f4f1db92629e6e30417beb8ae262662
https://doi.org/10.1007/978-3-031-27250-9_24
https://doi.org/10.1007/978-3-031-27250-9_24
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031272493
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82a3a8f3e4b9227f7a7adcad934634b0
https://doi.org/10.1007/978-3-031-27250-9_20
https://doi.org/10.1007/978-3-031-27250-9_20
Publikováno v:
IEEE Transactions on Fuzzy Systems. 29:2145-2157
As an important branch in the field of soft computing, TSK fuzzy systems have been diversely applied to supervised learning in recent years. However, real-world data may contain label noise, which has a negative impact on supervised learning. Label n
Publikováno v:
2022 IEEE Congress on Evolutionary Computation (CEC).
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference Companion.