An ensemble of differential evolution and Adam for training feed-forward neural networks

Adam is an adaptive gradient descent approach that is commonly used in back-propagation (BP) algorithms for training feed-forward neural networks (FFNNs). However, it has the defect that it may easily fall into local optima. To solve this problem, some metaheuristic approaches have been proposed to...

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Vydáno v:	Information sciences Ročník 608; s. 453 - 471
Hlavní autoři:	Xue, Yu, Tong, Yiling, Neri, Ferrante
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.08.2022
Témata:	Differential algorithms Ensemble algorithms Feed-forward neural networks Gradient-based algorithms Multi-populations Ensemble algorithms Gradient-based algorithms Differential algorithms Feed-forward neural networks Multi-populations
ISSN:	0020-0255, 1872-6291
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