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Erschienen in:
Introduction to Evolutionary Single-Objective Optimisation Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

9. Evolutionary Deep Neural Networks

verfasst von : Seyedali Mirjalili

Erschienen in: Evolutionary Algorithms and Neural Networks

Verlag: Springer International Publishing

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Abstract

This chapter first employs a kinematic model of hand to create two datasets for static hand postures. Neural Networks with different learning algorithms are then applied to the datasets for classification. The chapter also considers the comparison and analysis of different evolutionary algorithms for classifying datasets as well. Another contribution is finding the best set of features for the dataset using evolutionary algorithms. The results show that due to the large number of samples and features, Back Propagation is not effective due to the problem of local optima stagnation. However, Evolutionary Algorithms are able to efficiently classify the dataset with a very high accuracy and convergence speed. It was also observed that feature selection is important and evolutionary algorithms are able to find the optimal set of features for this problem.

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Vorheriges Kapitel Evolutionary Radial Basis Function Networks
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Metadaten
Titel
Evolutionary Deep Neural Networks
verfasst von
Seyedali Mirjalili
Copyright-Jahr
2019
Buch
Evolutionary Algorithms and Neural Networks

Print ISBN: 978-3-319-93024-4

Electronic ISBN: 978-3-319-93025-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-93025-1_9

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