## 5 Experimental Design and Result Analysis
### Optimizing Meta-Parameters of SVM Using GA  
The basic idea of supporting vector machine is to find a hyperplane to maximize the classification interval in order to obtain the highest classification accuracy. For the data with nonlinear separability, the samples in the input space can be mapped to the high-dimensional feature space by nonlinear transformation. The nonlinear classification problem is transformed into the linear classification problem. This is nonlinear SVM. This kind of nonlinear transformation is usually realized by kernel function. The kernel functions of support vector machines are varied. Different kernel functions are accompanied by different nonlinear mapping methods with different hyperparameters. Common kernel functions include polynomial function, radial basis function (RBF), Sigmoid function, and so on. The mathematical expression of RBF kernel function [30] is  
\[K(x_{i}\cdot x)=\exp\left(-\frac{\left\|x_{i}-x\right\|^{2}}{2\sigma^{2}}\right) \tag{17}\]  
where \(\sigma\) is a free parameter to indicate the variance of kernel. Therefore, for SVM using the RBF kernel function, there are two parameters to be determined, \(\sigma\) and penalty factor \(C\). Generally speaking, with the increase in \(C\), SVM will reduce the misclassification of training data. However, it will increase the possibility of over-fitting. On the contrary, if the value of \(C\) is too small, SVM is easy to underfit, affecting the classification performance. Thus, it can be seen that the selection of meta-parameter values is very important to the performance of support vector machine. As a classical parameter optimization method, genetic algorithm (GA) is based on biological evolution [31]. Compared with other types of heuristic algorithms (such as particle swarm optimization), it has good convergence, robustness and high accuracy. This method has a wide range of applications, strong expansibility, and is easy to combine with other methods. The research shows that this method has the possibility of parallel computing. If implemented, this will greatly improve the computing speed and the robustness of the algorithm. This is also the main research topic of the GA method at present. In this study, the GA algorithm is used to select SVM meta-parameters, and the method flow is shown in the Figure 8.  
Figure 7: Schematic diagram of field audio signal acquisition process.  
When using the GA algorithm to select SVM meta-parameters, we should first confirm the value range of GA meta-parameters, including the maximum number of iterations, the amount of primary population, mutation probability, etc. Then, the training samples are binary coded and evenly divided into five equal parts, using the method of cross-validation to maximize the generalization ability of the training model. After repeated genetic iterations, the optimal individual of the last generation population was obtained. Finally, after the decoding operation, the optimal parameters of best \(C\) and best \(\sigma\) are obtained. In the optimization process, the penalty factor \(C\) is set at (0,200) and \(\sigma\) is set at (0,0.25). Figure 9 shows the optimization process of a set of experimental data. The results show that the SVM meta-parameter selection method based on GA can effectively improve the generalization ability of the support vector machine.  
Figure 8: Flowchart of GA to optimize parameters \(C\) and \(\sigma\) of SVM.