# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 5 Results And Discussion
### Classification Performance
In all testing scenarios, including facial expression classification (FEI_1, FEI_2, and JAFFE) and a facial recognition dataset  
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**Soments** & **Methods** & **FEI\_3** & **W-Tent** & **FEI\_2** & **W-Tent** & **HOWER** & **W-Tent** & **JFFE** & **W-Tent** & **K  
(ORL), the performance of ITACIE-GP was significantly superior to these five deep feature-based methods. However, in original, blurred, and occlusion scenarios, the ITACIE-GP method's performance was comparable to or slightly inferior to that of MNASNet, VGG, and ResNet. This may be due to the ability of deep feature-based methods to learn distinctive features during the classification of flowers in original, blurred, and occlusion scenarios, features that are not easily affected by such adverse factors. In contrast, in low-light and noisy scenarios, the low contrast and noise in images significantly impair the performance of these methods. Although ITACIE-GP may not outperform the deep feature-based methods in original, blurred, and occlusion scenarios, it still demonstrates similar results, and significantly surpasses these methods in the other two challenging scenarios, showcasing the robustness of the ITACIE-GP method. On the KTH dataset, the ITACIE-GP's performance in original scenarios was not as good as all the deep feature methods referenced, but in blurred and noisy scenarios, its performance was comparable to or slightly inferior to some of the deep feature methods. It is particularly noteworthy that under scenarios of low contrast and occlusion, ITACIE-GP significantly outperforms the five deep feature methods, highlighting the potential instability of deep feature approaches when dealing with image datasets under adverse scenarios. At the same time, the design goal of ITACIE-GP to maintain good performance even in various challenging scenarios was significantly demonstrated. In the 30 comparisons with EFLGP, the ITACIE-GP method demonstrated a significant advantage in the majority of comparisons, specifically in 28 out of 30. In the 2 comparisons under low contrast scenarios, its performance was similar to that of EFLGP. This advantage mainly results from the program structure based on information transmission designed in this paper, which ensures that the initialized solutions have higher performance. Moreover, thanks to the accompanying evolution strategy, the classification accuracy continues to improve even in the later stages of the evolutionary process. Figures 5-9 illustrate the convergence curves of the average classification accuracy of the best individuals in each generation during 30 runs of EFLGP and ITACIE-GP across five datasets, FEI_1, FLOWER, JAFFE, KTH, and ORL, under five different scenarios. The figures demonstrate that the innovative program structure of ITACIE-GP enables optimal individuals to achieve high classification performance on the training set from the very beginning. As the evolutionary process progresses, especially in the middle and later stages, ITACIE-GP continues to enhance the training set classification performance of the best individuals due to its accompanying evolution strategy. In contrast, the training set classification performance of the best individuals in EFLGP might slow down or even stagnate after a certain generation, indicating that it may be less effective in continuous optimization compared with ITACIE-GP. Overall, ITACIE-GP uses its outstanding program structure to attain superior initial training set classification performance and continuously improves this performance through its evolutionary strategy. Figure 5: Cont. Figure 5: Convergence curves of EFLGP and ITACIE-GP on the FEL_1 dataset in five different scenarios. Figure 6: Convergence curves of EFLGP and ITACIE-GP on the Flower dataset in five different scenarios. Figure 8: \(Cont\). Figure 7: Convergence curves of EFLGP and ITACIE-GP on the Jaffe dataset in five different scenarios.