# Genetic Algorithm-Based Intelligent Selection Method of Universal Shield Segment Assembly Points
## 5 Results and Discussion  
### Weights Optimization and Validation  
To evaluate the effectiveness of the proposed approach, 70% of the 11,570 example data is used for training and 30% for testing. Moreover, the parameter values of the genetic algorithm are listed in Table 4. The proposed genetic algorithm is programmed in Python, and the program runs on a computer with an environment of 6136 CPU, 3.0 GHZ frequency and 32 GB memory. The genetic algorithm optimizes the weights in 81 intervals, and the objective function is used to select the assembly points with a total correct rate of 92.6%. The training correct rate and examples of each interval are shown in Figure 12, and it can be seen that the examples are uniformly distributed in each interval, and the example size of each interval is within the range of [90; 110], which can provide confidence in the correct rate of the point selection and the optimized weights. The correct rate of all intervals is above 80%, but the correct rate of certain intervals is high, such as interval 9, 73, etc. All reach 100%, and the correct rate of the certain interval is low, such as 80.2% for interval 51. This is because when the drivers make the labels, the degree of focus on optimizing the different WCPs is continuously changing, and the weights should theoretically change continuously. However, we divide the whole working condition into 81 intervals with defined interval boundary values and share the same weights within the interval. This approach leads to assembly point selection results for some transition data around interval boundary values that do not match the labels, resulting in a slightly lower correct rate in certain intervals. However, in practical engineering, these transition data often have two or even more optional assembly points. Even though the objective function selected points are different from the labels, they are still available. Figure 13 shows the genetic algorithm iteration diagrams of the four intervals 9, 41, 73 and 81. From the four iteration diagrams, it can be seen that the maximum assembly point selection correct rate (the maximum correct rate in a population) increases with the number of iterations and remains stable after reaching the maximum correct rate. In addition, the average correct rate (the average correct rate of a population) also gradually approaches the maximum correct rate of assembly point selection with the increase of the number of iterations and finally reaches convergence, which proves the convergence of the algorithm. It can also be noted that the relatively small number of steps and faster convergence of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Values** \\ \hline \(M\) & number of chromosomes in population & 30 \\ \hline \(m\) & number of genes in a chromosome & 7 \\ \hline _MaxIter_ & maximum number of iterations & 100 \\ \hline \(P_{c}\) & probability of crossover & 0.9 \\ \hline \(P_{m}\) & probability of mutation & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of genetic algorithm parameters. the genetic algorithm is due to the fact that a number of weights can be used as optimal weights to achieve the selection of best assembly point from four to five possible assembly points. Figure 12: Training correct rate of 81 intervals. Figure 13: Genetic algorithm iteration diagrams: (**a**) interval 9 iteration diagram; (**b**) interval 41 iteration diagram; (**c**) interval 73 iteration diagram; and (**d**) interval 81 iteration diagram. Interval 9 corresponds to working conditions where \(|U_{h}(n)|\) and \(|U_{v}(n)|\) are good, while \(|T_{h}(n)|\) and \(|T_{v}(n)|\) are poor, and the weights are 0.06, 0.03, 0.47 and 0.44, respectively. Interval 73 corresponds to working conditions where \(|U_{h}(n)|\) and \(|U_{v}(n)|\) are poor, while \(|T_{h}(n)|\) and \(|T_{v}(n)|\) are good, and the optimized weights are 0.45, 0.40, 0.06 and 0.09, respectively.