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Evolutionary Deep Neural Networks

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Evolutionary Algorithms and Neural Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 780))

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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Authors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

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  1. Seyedali Mirjalili
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Correspondence to Seyedali Mirjalili .

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Mirjalili, S. (2019). Evolutionary Deep Neural Networks. In: Evolutionary Algorithms and Neural Networks. Studies in Computational Intelligence, vol 780. Springer, Cham. https://doi.org/10.1007/978-3-319-93025-1_9

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