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An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

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conference contribution
posted on 11.02.2021, 02:25 by Ying Bi, Bing Xue, Mengjie Zhang
© 2019 IEEE. Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP.

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Preferred citation

Bi, Y., Xue, B. & Zhang, M. (2019, June). An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings 2019 IEEE Congress on Evolutionary Computation (CEC) (00 pp. 3197-3204). IEEE. https://doi.org/10.1109/CEC.2019.8790151

Conference name

2019 IEEE Congress on Evolutionary Computation (CEC)

Conference start date

10/06/2019

Conference finish date

13/06/2019

Title of proceedings

2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Volume

00

Publication or Presentation Year

01/06/2019

Pagination

3197-3204

Publisher

IEEE

Publication status

Published

Exports

Conference papers

Categories

Exports