## 4 Experiments and Discussion
Late stage (10-20 generations): From the 10 th generation, Cultural Color IGA continues to lead the fitness growth rate, reaching 9.28 in the 20 th generation, while IGA is 8.35. This result shows that Cultural Color IGA can improve fitness faster in the later stages, significantly improving the efficiency of the optimization process and reducing the number of trials and errors in the design. Overall comparison: From the average of all algebras, the average maximum fitness of Cultural Color IGA is about 9.03% higher than that of traditional IGA, especially in the last 10 generations, the average maximum fitness of Cultural Color IGA is about 13.37% higher than that of IGA. It further embodies its efficient optimization ability. It shows the significant advantages of the stacking agent model in reducing the burden of manual evaluation and improving the optimization efficiency. This model provides important support for the sustainable inheritance of cultural heritage design by reducing resource consumption and improving design efficiency. Figure 21 compares the number of user reviews required for a traditional IGA versus a Cultural Color IGA. From the figure, we can observe the following points:  
1. Number of reviews for traditional IGA: The traditional IGA algorithm requires a large number of user reviews, totaling about 500. This high-frequency evaluation requirement stems from the fact that traditional IGA relies on direct feedback from users for fitness evaluation in each generation, which greatly increases the workload of users;
2. Evaluation times of Cultural Color IGA: After the introduction of the stacking agent model, the user evaluations of Cultural Color IGA are reduced to about 163 times. The stacking proxy model is trained with large amounts of data to predict and simulate user evaluations of color schemes, replacing direct user evaluations in most generations. This significantly reduces the number of times users actually participate in the evaluation, alleviates user fatigue, and reduces the workload. 3. Impact of reduced reviews: Cultural Color IGA has approximately 67.4% fewer user reviews compared to traditional IGA. This significant reduction not only improves the efficiency of the algorithm but also provides a more user-friendly evaluation experience for users. This approach is particularly important in the process of generating color schemes for the preservation of cultural heritage, as it represents an efficient and sustainable approach to design optimization while ensuring design quality and significantly reducing manual input. Figure 20: Comparison of the average maximum fitness. Figure 22 shows the distribution of the average fitness values of 10 users with the increase in algebra when the Cultural Color IGA algorithm is used. By analyzing the figure, we can observe the following points:  
1. Overall upward trend: The 10 user fitness curves in the figure show an overall upward trend, indicating that the Cultural Color IGA algorithm is continuously improving the fitness value of each user with the increase in algebra. This gradual upward trend reflects that the algorithm continues to optimize the design scheme of each user, making the fitness improve from generation to generation;
2. Fluctuations and peaks: In the rising process, each curve has certain fluctuations, and the average fitness value is not monotonically increased but accompanied by certain ups and downs. This fluctuation may be due to the fitness changes of the algorithm in the process of searching for different solutions, reflecting the dynamic adjustment ability of Cultural Color IGA between local solutions and global solutions. Each curve also exhibits some peaks in different algebras, and these peaks correspond to relatively high fitness solutions found by the algorithm in these algebras;
3. Distribution range of fitness: In the evolution process from 1 to 20 generations, the fitness value gradually increased from close to 3 points to more than 7 points in 20 generations.