Soft Computing

, Volume 23, Issue 23, pp 12859–12871 | Cite as

Genetic programming with transfer learning for texture image classification

  • Muhammad Iqbal
  • Harith Al-SahafEmail author
  • Bing Xue
  • Mengjie Zhang
Methodologies and Application


Genetic programming (GP) represents a well-known and widely used evolutionary computation technique that has shown promising results in optimisation, classification, and symbolic regression problems. However, similar to many other techniques, the performance of GP deteriorates for solving highly complex tasks. Transfer learning can improve the learning ability of GP, which can be seen from previous research on including, but not limited to, symbolic regression and Boolean problems. However, utilising transfer learning to tackle image-related, specifically, image classification, problems in GP is limited. This paper aims at proposing a new method for employing transfer learning in GP to extract and transfer knowledge in order to tackle complex texture image classification problems. To assess the improvement gained from using the extracted knowledge, the proposed method is examined and compared against the baseline GP method and a state-of-the-art method on three publicly available and commonly used texture image classification datasets. The obtained results indicate that the reuse of the extracted knowledge from an image dataset has significant impact on improving the performance in learning different rotated versions of the same dataset, as well as other related image datasets. Further, it is found that the proposed approach in the very first generation of the evolutionary process produces better classification accuracy than the final classification accuracy obtained by the baseline method after 50 generations.


Genetic programming Transfer learning Image classification Code fragments Evolutionary computation 



This work is supported in part by the Marsden Fund (Contract Numbers VUW1509, VUW1615) of New Zealand, and the University research grant of Victoria University of Wellington (Grant Numbers 213150 and 216137). Bing Xue received research grants from Marsden Fund (VUW1615) of New Zealand and from Victoria University of Wellington (213150). Mengjie Zhang received the research grants from Marsden Fund of New Zealand (VUW1509) and Victoria University of Wellington (216137).

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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