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A hybrid differential evolution approach to designing deep convolutional neural networks for image classification

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conference contribution
posted on 29.10.2020, 00:48 by Bin Wang, Y Sun, Bing Xue, Mengjie Zhang
© Springer Nature Switzerland AG 2018. Convolutional Neural Networks (CNNs) have demonstrated their superiority in image classification, and evolutionary computation (EC) methods have recently been surging to automatically design the architectures of CNNs to save the tedious work of manually designing CNNs. In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN. There are three new ideas in the proposed DECNN method. Firstly, an existing effective encoding scheme is refined to cater for variable-length CNN architectures; Secondly, the new mutation and crossover operators are developed for variable-length DE to optimise the hyperparameters of CNNs; Finally, the new second crossover is introduced to evolve the depth of the CNN architectures. The proposed algorithm is tested on six widely-used benchmark datasets and the results are compared to 12 state-of-the-art methods, which shows the proposed method is vigorously competitive to the state-of-the-art algorithms. Furthermore, the proposed method is also compared with a method using particle swarm optimisation with a similar encoding strategy named IPPSO, and the proposed DECNN outperforms IPPSO in terms of the accuracy.

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Preferred citation

Wang, B., Sun, Y., Xue, B. & Zhang, M. (2018, January). A hybrid differential evolution approach to designing deep convolutional neural networks for image classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (11320 LNAI pp. 237-250). Springer International Publishing. https://doi.org/10.1007/978-3-030-03991-2_24

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11320 LNAI

Publication or Presentation Year

01/01/2018

Pagination

237-250

Publisher

Springer International Publishing

Publication status

Published

ISSN

0302-9743

eISSN

1611-3349

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