## 4 Experiments and Discussion
After the experiment officially began, the Cultural Color IGA system first automatically rated the color schemes, with scores ranging from 0 to 10. Then, the experimental subjects adjusted the scores according to their personal aesthetic preferences. Figure 16 shows the evolution of some of the populations during the color-matching process. Figure 16a shows the initial population, while b, c, d, and e represent the population evolution of the 5 th, 10 th, 15 th, and 20 th generations, respectively. Figure 15: Analysis of color genes and initial scheme in Yungang Grottoes states. (**a**) Yungang Grottoes states; (**b**) color network model; (**c**) color genes; (**d**) initial color scheme. In order to show the color-matching effect produced under different patterns, the Yungang dragon pattern and chess pattern are taken as the center, the Yungang lotus pattern, honeysuckle pattern, and banana leaf pattern are extracted as decoration, and the flying sky image is surrounded by the color-matching effect, as shown in Figure 17. The resulting satisfactory color scheme is shown in Figure 18.  
Figure 16: Color scheme designed by a test subject. In order to verify the effectiveness of this method, traditional IGA, and Cultural Color IGA were compared. The parameter configuration of the two algorithms is unified as follows: a single-point crossover method is adopted, the population number is 25, the roulette selection method and elite strategy are adopted, the crossover probability \(P_{r}\) = 50%, the mutation probability \(P_{m}\) = 20%, and the elimination score is 7.5 [31]. The two methods were independently operated by 10 users once, and the population evolution ended after the 20 th generation. The experimental results are compared mainly from three aspects--average fitness, average maximum fitness, and evaluation times--so as to compare the optimization ability of the algorithm. To ensure a fair comparison, the two methods used the same reference source images to avoid potential biases introduced by image differences. Figure 17: Yungang dragon pattern and plain chessboard design color scheme. Figure 18: Satisfactory color scheme. Figure 19 shows the change trend of average fitness between the traditional interactive genetic algorithm (IGA) and the interactive genetic algorithm (Cultural Color IGA) that introduces the color harmony prediction model, aiming to evaluate how to improve the efficiency of color application by improving the algorithm, so as to promote the sustainability of cultural heritage conservation. Initial stage (1-5 generations): In the early stage of the algorithm, the average fitness change trend of Cultural Color IGA and traditional IGA is relatively close, the fitness fluctuates between 3 and 4, and the growth rate of both is similar. This phase reflects the preliminary optimization of the model, indicating that although the stacking agent model has been embedded in the algorithm, its advantages are not fully apparent, and no significant performance gaps are visible. In the middle stage (5-10 generations), the average fitness of Cultural Color IGA increases rapidly, and by the 10 th generation, Cultural Color IGA has surpassed the traditional IGA, reaching a fitness of about 7, while the traditional IGA's fitness is about 6. At this time, the role of the stacking agent model gradually appears, which promotes the optimization process of algorithms, reduces human interference in design evaluation, and provides more efficient and sustainable design optimization support. Middle stage (5-10 generations): Starting with the 5 th generation, the maximum average fitness of Cultural Color IGA gradually exceeds that of IGA and opens the gap by the 10 th generation, reaching about 7.5 compared to about 6.8 for IGA. At this stage, the stacking agent model gradually shows advantages, which improves the efficiency of fitness growth and enables algorithms to approach high-fitness solutions more quickly.