Multi-chromosomal CGP-evolved RNN for signal reconstruction

A multi-chromosomal structure for neuro-evolution of recurrent neural networks (RNNs) with Cartesian genetic programming (CGP) architecture is presented to develop a signal reconstruction model. The group behaviour of multi-chromosomes evolved together is explored in terms of computational and struc...

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Veröffentlicht in:	Neural computing & applications Jg. 33; H. 20; S. 13265 - 13285
Hauptverfasser:	Khan, Nadia Masood, Khan, Gul Muhammad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.10.2021 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Cartesian coordinates Chromosomes Computational Biology/Bioinformatics Computational Science and Engineering Computer architecture Computer Science Data Mining and Knowledge Discovery Deep learning Genetic algorithms Image Processing and Computer Vision Machine learning Neural networks Optimization Original Article Probability and Statistics in Computer Science Programming Recurrent neural networks Signal reconstruction Speech Neuro-evolution Signal reconstruction Recurrent neural network Linear Regression Machine learning Random Forest
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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