Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. Howev...

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Veröffentlicht in:	Neural processing letters Jg. 55; H. 4; S. 4519 - 4622
Hauptverfasser:	Kaveh, Mehrdad, Mesgari, Mohammad Saadi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Belgium Springer Nature B.V 01.08.2023 Springer US
Schlagworte:	Accuracy Algorithms Artificial intelligence Artificial neural networks Back propagation Back propagation networks Bibliometrics Continuity (mathematics) Cost function Data science Deep learning Exploitation Genetic algorithms Heuristic Heuristic methods Literature reviews Machine learning Mathematical analysis Neural networks Optimization Optimization algorithms Parameters Research methodology Taxonomy Training And gradient-based back propagation (BP) learning algorithm Artificial neural networks (ANN) Deep learning (DL) Hyper-parameters optimization Meta-heuristics (MH)
ISSN:	1370-4621, 1573-773X
Online-Zugang:	Volltext
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