## 3 Methodology
### Cultural Color IGA System Technical Architecture  
Based on the traditional IGA, the technical architecture has been optimized, as detailed in Table 2.  
Based on the analysis in Table 2, we further refine the evolutionary operation process of Cultural Color IGA, as illustrated in Figure 5.  
First, a cultural color network model is generated based on cultural heritage images provided by the user. Since the color network model reflects the color-matching patterns of a specific culture, a set of color nodes can be selected from it as the initial color genes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Function and Implementation** & **Key Features** \\ \hline Platform Selection & Implement color extraction, optimization, and display processes using the Python platform. & Modular development and efficient support for color optimization tasks. \\ Use of Color Reference Sources & Use images provided by designers as reference sources to extract color network model and generate initial color genes through the color network model. & Combining traditional culture with modern applications, ensuring the richness and diversity of the schemes. \\ Designer Interaction Design & The system automatically evaluates the harmony of the color schemes through machine learning models, allowing designers to adjust and select their preferred schemes, reducing workload. & Enhances efficiency, optimizes the interaction experience, and ensures optimization results. \\ Genetic Algorithm & The genetic algorithm gradually optimizes the color schemes through selection, crossover, and mutation operations with multiple iterations, balancing traditional aesthetics and modern innovation. & Ensures the practicality of the optimized results, making the schemes more culturally valuable and innovative. \\ Color Scheme Functionality & Color schemes are displayed intuitively, supporting designers in comparing, adjusting, and optimizing. & Quickly generates high-quality schemes, lowers the design threshold, and meets multi-level user needs. \\ \hline \hline \end{tabular}
\end{table}
Table 2: System technical architecture.  
Figure 4: Model performance horizontal bar chart.  
Next, the user designs the line drawing of the object to be colored and defines color zones (specific zones designated for specific colors). The designer then applies the initial color genes to create the first color scheme. To ensure clear segmentation, the system uses the K-means clustering algorithm to divide the image into color zones. These zones serve as the \"color structures\" recognizable by the interactive genetic algorithm (IGA), where different color structures correspond to different color schemes.  
The system then initializes the first population using IGA, incorporating a stacking model as the fitness evaluation proxy to predict the color harmony score of each scheme. Fitness scores are calculated, and designers can manually adjust certain evaluations before executing crossover and mutation operations to optimize the color scheme.  
As evolutionary generations progress, the process stops once a predefined termination condition is met, or a satisfactory color scheme is obtained. This method enables rapid evaluation of color schemes, reduces the workload for designers, and ensures that even less experienced designers can create high-quality cultural heritage color schemes.