Evolutionary deep neural networks

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages16
Publication statusPublished - 1 Jan 2019
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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