# Interactive 3D Vase Design Based on Gradient Boosting Decision Trees
## 4 Experimental Results and Analysis
### Comparison of User Fatigue Alleviation  
The IGA optimizes problem-solving through user interaction to address issues that cannot be defined with formulas. However, a core issue with IGAs is user fatigue as prolonged evaluations can lead to fatigue. When users feel fatigued, their efficiency and productivity may decrease. Fatigue affects concentration and attention, making it harder for users to focus on tasks, thereby reducing overall system efficiency. This paper uses LGBM-IGA and CAT-IGA algorithms to predict vase fitness values, fitting user evaluation values to reduce the number of operations and alleviate user fatigue. To evaluate the proposed algorithm's effectiveness in alleviating user fatigue, experiments were conducted to record the evaluation numbers and evaluation time for five users using six algorithms. In this paper, evaluation numbers refer to the count of evaluations a user performs on individuals in the population, while evaluation time reflects the duration time users spend in the design process, system running speed, and algorithm performance. With increasing evaluation numbers and longer evaluation time, users may experience fatigue. Therefore, comparing these metrics across different algorithms can better assess the effectiveness of the proposed algorithm in mitigating user fatigue. Specific data are shown in Table 5.  
Figures 12 and 13 show the mean and standard deviation of evaluation numbers and evaluation time for five users on the vase design platform using IGA, KDT-IGA, RF-IGA, XGB-IGA, LGBM-IGA, and CAT-IGA. From Figures 12 and 13, it can be seen that IGA algorithms with proxy models have lower average evaluation numbers and a shorter evaluation time than a traditional IGA, with LGBM-IGA and CAT-IGA exhibiting the lowest average evaluation numbers and the shortest evaluation time among the proxy models. Compared to the IGA, the evaluation numbers were reduced by 74 and 72.4, and the evaluation time was reduced by 233 s and 230 s, respectively. The standard deviations of the evaluation numbers and evaluation time for LGBM-IGA and CAT-IGA are smaller than those for other proxy models, indicating more consistent evaluation numbers and evaluation time across different users. Since the evaluation numbers for the IGA were the same, the standard deviation calculation result was 0. Comparing the mean and standard deviation of the evaluation numbers and evaluation time for the six algorithms, it is evident that proxy model IGAs have an advantage in mitigating user fatigue over traditional IGAs. Among them, LGBM-IGA and CAT-IGA show lower mean and standard deviations, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**User Number**} & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{2}{*}{IGA} & evaluation numbers & 120 & 120 & 120 & 120 & 120 \\  & evaluation time & 511 s & 394 s & 478 s & 354 s & 468 s \\ \hline \multirow{2}{*}{KDT-IGA} & evaluation numbers & 73 & 66 & 57 & 54 & 42 \\  & evaluation time & 369 s & 407 s & 370 s & 322 s & 341 s \\ \hline \multirow{2}{*}{RF-IGA} & evaluation numbers & 68 & 65 & 83 & 31 & 36 \\  & evaluation time & 340 s & 341 s & 314 s & 286 s & 292 s \\ \hline \multirow{2}{*}{XGB-IGA} & evaluation numbers & 58 & 60 & 30 & 42 & 75 \\  & evaluation time & 358 s & 236 s & 270 s & 242 s & 248 s \\ \hline \multirow{2}{*}{LGBM-IGA} & evaluation numbers & 40 & 32 & 60 & 52 & 46 \\  & evaluation time & 205 s & 196 s & 230 s & 213 s & 196 s \\ \multirow{2}{*}{CAT-IGA} & evaluation numbers & 36 & 54 & 45 & 56 & 47 \\  & evaluation time & 192 s & 221 s & 195 s & 226 s & 221 s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of evaluation numbers and evaluation time. demonstrating higher stability and effectively reducing user fatigue. This indicates that LGBM-IGA and CAT-IGA are more effective compared with other algorithms in alleviating user fatigue, thereby enhancing algorithm performance and user experience. Figure 12: Mean value and standard deviation of evaluation numbers.