Zobrazeno 1 - 10
of 343
pro vyhledávání: '"Andries P. Engelbrecht"'
Publikováno v:
Applied Sciences, Vol 14, Iss 21, p 9976 (2024)
The performance of the differential evolution algorithm (DE) is known to be highly sensitive to the values assigned to its control parameters. While numerous studies of the DE control parameters do exist, these studies have limitations, particularly
Externí odkaz:
https://doaj.org/article/56e4f68cf05349c9995a0c76f14307e1
Publikováno v:
Algorithms, Vol 14, Iss 3, p 100 (2021)
This study presents a novel performance metric for feature selection algorithms that is unbiased and can be used for comparative analysis across feature selection problems. The baseline fitness improvement (BFI) measure quantifies the potential value
Externí odkaz:
https://doaj.org/article/16c69f57b7ad4e5caee007dfa6f7e945
Publikováno v:
Algorithms, Vol 14, Iss 2, p 36 (2021)
Multimodal problems are single objective optimisation problems with multiple local and global optima. The objective of multimodal optimisation is to locate all or most of the optima. Niching algorithms are the techniques utilised to locate these opti
Externí odkaz:
https://doaj.org/article/35e377cc6cfc4e19997de1bd5579bc60
Publikováno v:
Swarm Intelligence. 16:143-168
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59622d87eb4df5d0b27bcd4c67d61e6a
https://doi.org/10.1007/s11721-022-00210-3
https://doi.org/10.1007/s11721-022-00210-3
Publikováno v:
2022 IEEE Symposium Series on Computational Intelligence (SSCI).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e44dda4283f045ef262d64e38625c18
https://doi.org/10.1109/ssci51031.2022.10022110
https://doi.org/10.1109/ssci51031.2022.10022110
Publikováno v:
Neurocomputing. 466:252-264
This paper empirically analyses the impact of changes to the set of training examples on the neural network error surface. Specific quantitative characteristics of the error surface related to properties such as ruggedness, modality and structure are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bf41072b881d6a2476401a79a99150a6
https://doi.org/10.1016/j.neucom.2021.09.023
https://doi.org/10.1016/j.neucom.2021.09.023
Publikováno v:
Information Sciences. 569:615-649
SDCP require classifiers with the ability to learn and to adjust to the underlying relationships in data streams in real-time. This requirement poses a challenge to classifiers, because the learning task is no longer just to find the optimal decision
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb0624d2f602b36d4506deaeb8664bb3
https://doi.org/10.1016/j.ins.2021.05.023
https://doi.org/10.1016/j.ins.2021.05.023
Publikováno v:
2022 IEEE Congress on Evolutionary Computation (CEC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55a5f7c97256c024165a3040d7cef784
https://doi.org/10.1109/cec55065.2022.9870360
https://doi.org/10.1109/cec55065.2022.9870360
The multi-guide particle swarm optimisation (MGPSO) algorithm, originally developed for static multi-objective optimisation problems (SMOPs), has been recently adapted for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-sw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::779d039bde06a2876210ac24286eca14
https://doi.org/10.21203/rs.3.rs-1503527/v1
https://doi.org/10.21203/rs.3.rs-1503527/v1
Publikováno v:
Neurocomputing. 400:113-136
Quantification of the stationary points and the associated basins of attraction of neural network loss surfaces is an important step towards a better understanding of neural network loss surfaces at large. This work proposes a novel method to visuali
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8b61a89d47003e27a3554264fbc9a86
https://doi.org/10.1016/j.neucom.2020.02.113
https://doi.org/10.1016/j.neucom.2020.02.113