## 5 Conclusions  
This paper combines the color network model and the color harmony prediction model to propose an innovative Cultural Color IGA algorithm for the optimized design of cultural heritage color regeneration. By integrating interactive genetic algorithms, culturally contextual color networks, and color harmony prediction models, this approach can automatically generate color schemes that are both in line with cultural heritage styles and visually harmonious, significantly improving the efficiency and quality of color design. First, this paper designs a multi-layered color relationship network to accurately model the interrelationships between colors and their cultural contexts, providing strong data support for the regeneration of cultural heritage colors. Second, the proposed color harmony prediction model plays a key role in the design process. By training a deep learning model on large-scale datasets, we significantly enhance the accuracy and computational efficiency of color-matching. A stacking-based multi-model ensemble method is used to predict color harmony, effectively improving both the precision and stability of the predictions. Compared to traditional methods, the ensemble model handles complex color combinations better, optimizes the computational efficiency in the color design process, and reduces the need for manual intervention. In terms of algorithm optimization, this paper combines the strengths of genetic algorithms to simulate the processes of natural selection and gene mutation, optimizing the generation of color schemes. The Cultural Color IGA can quickly converge to high-harmony design solutions within a few generations. Experimental results show that this method significantly outperforms traditional genetic algorithms in terms of maximum fitness and average fitness. The color schemes it generates not only meet the cultural heritage style requirements but also exhibit high color harmony and visual appeal. Through comparative experiments, this paper validates the advantages of Cultural Color IGA in image color-matching, particularly in the context of cultural heritage applications. The method can accurately reproduce traditional colors according to the specific requirements of different cultural backgrounds while also aligning the design outcomes with modern aesthetic demands. Experimental results indicate that Cultural Color IGA generates higher-quality color schemes in fewer iterations compared to traditional methods, and the user evaluations are generally favorable. Specifically, 80.6% of participants were  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{U}\) & \(\mathbf{v1}\) & \(\mathbf{v2}\) & \(\mathbf{v3}\) & \(\mathbf{v4}\) & \(\mathbf{v5}\) \\ \hline \(u1\) & 0.29 & 0.51 & 0.18 & 0.02 & 0 \\ \(u2\) & 0.31 & 0.47 & 0.12 & 0.06 & 0.04 \\ \(u3\) & 0.37 & 0.46 & 0.15 &satisfied with the generated color schemes, believing they both respect the uniqueness of cultural heritage and possess modern design appeal. Most users felt that the color schemes generated by the algorithm effectively merged tradition with modernity, preserving cultural heritage while meeting contemporary design expectations. This provides new ideas and methods for the digital preservation of cultural heritage colors. Overall, Cultural Color IGA offers an innovative technical pathway for the regeneration of cultural heritage colors, enabling not only the automation of color design but also the improvement of design quality and efficiency. This method is applicable not only to the regeneration of cultural heritage colors but also offers potential applications in other artistic fields such as fashion, industrial design, and digital media. Future research could further optimize and expand the work presented in this paper in the following ways:  
Optimization of the color harmony prediction model: The current model still has limitations in handling certain complex color combinations.