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...

Full description

Bibliographic Details

Published in:	Information sciences Vol. 608; pp. 453 - 471
Main Authors:	Xue, Yu, Tong, Yiling, Neri, Ferrante
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.08.2022
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!