## 3 Methodology
### Construction of Color Harmony Prediction Model  
#### 3.2.1 Model Construction Preparation  
Interactive genetic algorithms (IGA) can apply patterns [19], shapes [20], colors [21], and structural designs [22] from cultural heritage to modern products, optimizing designs with cultural characteristics through user interaction. For instance, elements from the Yungang Grottoes are incorporated into contemporary products, architecture, or fashion designs, bringing new life to this heritage in a modern context. This innovative application promotes the sustainable transmission of cultural heritage, imbuing it with new meaning in modern life. However, traditional interactive genetic algorithms (IGA) often rely on subjective judgment from designers to evaluate fitness, which not only increases the workload of the designers but also leads to fatigue, thus affecting design efficiency [23; 24; 25; 26]. To improve the sustainability of the design process, researchers have introduced proxy evaluation models into IGAs [27; 28] to reduce the frequency of designer involvement in the evaluation, thereby enhancing the automation level and accuracy of the design process. The application of these proxy models not only improves efficiency during the design phase but also significantly lightens the workload of designers. Specifically, [PERSON] [29] and others established evaluation indicators based on color sequence and adjacency calculations for quick assessment of color-matching effects, reducing reliance on manual evaluations during the design process. However, their method still faced challenges such as insufficient sample size and model accuracy, especially in multi-dimensional feature and complex color-matching scenarios, where further optimization is needed. Figure 1 illustrates the main components of our framework and the training process. Our algorithm framework consists of several steps:  
Step 1: Based on the MTurk dataset, this study collected 10,743 color samples along with their corresponding rating data. Figure 1: Color harmony prediction algorithm framework. Step 2: A novel feature selection framework was developed, building upon the feature extraction methodologies of [PERSON] [15], [PERSON] [16], and [PERSON] [30]. The framework includes the following criteria:  
1. Validation from previous studies: Priority is given to effective features that have been verified in the research of [PERSON], [PERSON], and [PERSON];
2. Predictive performance significance: Features are iteratively evaluated throughout feature set construction and model training to assess their impact on predictive performance;
3. Statistical descriptiveness: Preference is given to features that accurately describe color distribution, variability, or complexity;
4. Relevance to human visual perception: Features must align with human color perception principles, such as the impact of hue, brightness, and saturation variations on visual experience;
5. Association with color harmony theories: Features should align with core principles of harmony theory, such as contrast, balance, and complexity. Based on these criteria, feature selection was conducted on the initial feature set, resulting in the extraction of 130 color features per sample from the MTurk dataset. These features include 107 color space features (such as the mean, standard deviation, minimum, maximum, range, skewness, and kurtosis of RGB, HSV, LAB, LCH, and CHSV spaces), 12 color-pair features (e.g., Delta E, Delta H), 4 entropy features (e.g., hue entropy, brightness entropy), 3 visual balance features (e.g., symmetry index, color distribution uniformity), 1 emotional feature (warm-cool contrast index), and 3 color aesthetic features (aesthetic measure M, order factor O, complexity factor C). These features were not only derived from previous research but also incorporated innovative applications of color harmony theories.