## 4 Experiments and Discussion  
Yungang Grottoes is one of the representatives of ancient Chinese cave art, and its sculptures have a deep cultural heritage and artistic value in their use of color, reflecting the uniqueness and richness of traditional Chinese art colors. Choosing a sufficient number of Yungang Grottoes images can provide ample color data, making the color network model more comprehensive when extracting color genes, thus avoiding insufficient representation due to a small sample size. Therefore, this study collected 120 images of Yungang Grottoes as samples through methods such as online downloads, book scans, and on-site photography. Based on data quality requirements, the selected images must meet the following principles:  
1. Images downloaded from the internet should be clear or relatively clear, with a resolution higher than 72 dpi;
2. For book scans, good natural lighting conditions should be selected, avoiding artificial light sources to reduce color distortion. A 50 mm prime lens should be used, with a shooting distance of 0.3-0.5 m, ensuring the clarity of image details and color restoration;
3. For on-site photography, even indoor lighting should be used, with a shooting distance of 0.5-1 m.  
Using the 120 Yungang Grottoes states as the reference source for color-matching, the color network model is extracted, and five nodes, 7, 8, 13, 15, and 17, are selected as the color genes for IGA. The color-matching design is demonstrated using the flat pattern in Figure 15d as an example. Figure 14: Mutation. The experimental subjects of the system's operation include 10 volunteers (five males and five females) who evaluate the generated color schemes. To ensure the scientific and representative nature of the research, the selection of these 10 volunteers takes into account diversity in gender, region, and professional background. Gender and Regional Distribution: The volunteers include five males and five females from Taiyuan, Beijing, Suzhou, Wuhan, and Guangzhou in China, covering different cultural backgrounds and geographical areas to ensure diversity in the rating results. Professional Composition: four art and design professionals, three art school teachers and students, two cultural and heritage conservation workers, and one representative from the general public. To ensure the accuracy and reliability of the evaluation results, we performed a two-step screening of the participants' color vision abilities. First, we asked participants if they had any known color vision impairments. Second, a strict color vision ability screening was conducted using the Ishihara color blindness test charts (Kanehara & Co., Tokyo, Japan). These steps ensured that all participants had no color blindness or color vision defects, thereby eliminating potential interference from insufficient color vision ability on the research results. In the experiment, volunteers completed the rating task based on their professional background and aesthetic preferences. The results were mainly compared in the following three aspects: average fitness (overall population optimization effect), average maximum fitness (degree of improvement in the optimal scheme), and user rating frequency (frequency of user participation in rating). By analyzing the operation results from diversified users, the strengths and weaknesses of the two algorithms in optimization capability and user interaction experience were comprehensively evaluated, providing a scientific basis for verifying the effectiveness of the intelligent color-matching method. This study strictly defined and controlled the rating scale. The rating range was from 0 to 10, where 0 represents complete disharmony, 10 represents perfect harmony, and 5 represents moderate harmony. Before rating, volunteers underwent a brief training to understand the specific meanings of the rating levels. To ensure the distinction and accuracy of the ratings, this study explicitly encouraged volunteers to make reasonable use of the rating range.