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A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks

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posted on 29.10.2020, 00:50 by B Wang, Y Sun, Bing Xue, Mengjie Zhang
© 2019, Springer Nature Switzerland AG. Image classification is a difficult machine learning task, where Convolutional Neural Networks (CNNs) have been applied for over 20 years in order to solve the problem. In recent years, instead of the traditional way of only connecting the current layer with its next layer, shortcut connections have been proposed to connect the current layer with its forward layers apart from its next layer, which has been proved to be able to facilitate the training process of deep CNNs. However, there are various ways to build the shortcut connections, it is hard to manually design the best shortcut connections when solving a particular problem, especially given the design of the network architecture is already very challenging. In this paper, a hybrid evolutionary computation (EC) method is proposed to automatically evolve both the architecture of deep CNNs and the shortcut connections. Three major contributions of this work are: Firstly, a new encoding strategy is proposed to encode a CNN, where the architecture and the shortcut connections are encoded separately; Secondly, a hybrid two-level EC method, which combines particle swarm optimisation and genetic algorithms, is developed to search for the optimal CNNs; Lastly, an adjustable learning rate is introduced for the fitness evaluations, which provides a better learning rate for the training process given a fixed number of epochs. The proposed algorithm is evaluated on three widely used benchmark datasets of image classification and compared with 12 peer Non-EC based competitors and one EC based competitor. The experimental results demonstrate that the proposed method outperforms all of the peer competitors in terms of classification accuracy.

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Preferred citation

Wang, B., Sun, Y., Xue, B. & Zhang, M. (2019). A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks. PRICAI 2019: Trends in Artificial Intelligence (11672 LNAI, pp. 650-663). Springer International Publishing. https://doi.org/10.1007/978-3-030-29894-4_52

Book title

PRICAI 2019: Trends in Artificial Intelligence

Publisher

Springer International Publishing

Pagination

650-663

Volume

11672 LNAI

ISBN

9783030298937

ISSN

0302-9743

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