# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 6 Conclusions  
In this paper, a new method based on GP is introduced for the classification of degraded images. The proposed program structure, based on information transmission, significantly enhances the potential of GP individuals. Under this structure, nodes in specific layers of GP individuals can automatically transmit effective information to nodes in subsequent layers, enabling the automatic construction of effective features. This significantly improves their performance as solutions for classification tasks. The accompanying evolution strategy effectively addresses the issue of fitness stagnation in GP individuals during the iterative process by employing storage and replacement strategies. This approach substitutes redundant individuals with those possessing the potential for development when population fitness stagnation occurs, thus directing the algorithm toward the identification of more superior individuals. Moreover, the application of the multi-generational  
Figure 11: Example program evolved from the JAFFE dataset through ITACIE-GP in the original scenarios, along with its two visualization examples. individual ensemble strategy, through the use of individual distances, incorporates several efficacious GP individuals into an ensemble solution, effectively mitigating the problem of overfitting. Leveraging these three strategies, the ITACIE-GP algorithm exhibits exceptional classification performance on datasets of original, blurry, low contrast, noisy, and occluded images. Experimental results indicate that ITACIE-GP consistently achieves similar or superior results, particularly in the classification of degraded images. Ablation studies further confirm the effectiveness and synergy of these strategies. Additionally, performance analyses elucidate the reasons behind the high-performance solutions evolved by ITACIE-GP. In the future, we will prioritize expanding classification examples that are closely aligned with real-world applications and develop more advanced technologies to enhance classification performance for degraded image tasks. Conceptualization, Y.S. and Z.Z. ; methodology, Z.Z. ; validation, Y.S. and Z.Z. ; formal analysis, Z.Z. ; investigation, Z.Z. ; resources, Z.Z. ; data curation, Z.Z. ; writing--original draft preparation, Z.Z. ; writing--review and editing, Y.S. and Z.Z. ; visualization, Z.Z. ; project administration, Y.S. ; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript. This research was supported by National Natural Science Foundation of China (Grant Nos. 61763002 and 62072124), Guangxi Major projects of science and technology (Grants No. 2020 AA21077021), Foundation of Guangxi Experiment Center of Information Science (Grant No. KF1401). Not applicable. The datasets used in this paper are available at the following URLs:  
FEI Face Database (accessed on 29 January 2024) ([[https://fei.edu.br/~cet/facedatabase.html](https://fei.edu.br/~cet/facedatabase.html)]([https://fei.edu.br/~cet/facedatabase.html](https://fei.edu.br/~cet/facedatabase.html))), Cambridge ORL Face Database (accessed on 29 January 2024) ([[http://cam-orl.co.uk/facedatabase.html](http://cam-orl.co.uk/facedatabase.html)]([http://cam-orl.co.uk/facedatabase.html](http://cam-orl.co.uk/facedatabase.html))), JAFFE Database (accessed on 28 January 2024) ([[https://www.kasr.org/jaffe_download.html](https://www.kasr.org/jaffe_download.html)]([https://www.kasr.org/jaffe_download.html](https://www.kasr.org/jaffe_download.html))), and KTH-TIPS Material Database (accessed on 6 February 2024) ([[https://www.csc.kth.se/cvap/databases/kth-tips/credits.html](https://www.csc.kth.se/cvap/databases/kth-tips/credits.html)]([https://www.csc.kth.se/cvap/databases/kth-tips/credits.html](https://www.csc.kth.se/cvap/databases/kth-tips/credits.html))).