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.
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