## 4 Algorithm Verification  
In order to verify the effectiveness of the proposed method, the general dataset [29] is applied to investigate the classification performance. Here, we select Thyroid, Ionosphere, Image and German datasets. The performance comparison between the adopted method and the traditional classification methods, as well as the bagging and boosting ensemble algorithms, are investigated. In reference [27], the paper selects 40 base classifiers and constructs the base classifiers based on the proposed method, bagging and boosting, respectively. Samples are resampled in the first layer five times. On this basis, the feature selection is carried out twice for each subset at the proportion of 80%. We obtained 10 training sets, SVM, KNN, BN and BPNN  
Figure 4: Flow chart of SQGA optimized ensemble learning. were adopted, and 40 base classifiers were obtained. Finally, the majority voting method was used to get the final classification results. Firstly, the convergence performance of SQGA was investigated. The parameters of each base classifier were arbitrarily set, as the learning ability of the selected learning machine is strong. In the SQGA optimization algorithm, the number of populations was set to 50 and iterations set to 100. The experimental results are shown in Figure 5.  
From Figure 5, we can see that, at the beginning, the classification accuracy of each dataset is relatively high. This shows that the ensemble learning algorithm can achieve better learning results, and the whole algorithm is stable. On different datasets, the algorithm converges rapidly, which shows that the convergence of the SQGA algorithm is better, and each algorithm is finally stable at a relatively high classification accuracy. In order to verify the SQGA's advantages of convergence, the QGA, genetic algorithm (GA) [30] and self-adaptive genetic algorithm (SAGA) [31, 32] were selected for comparison. The Image dataset was picked for testing, and the convergence curves are shown in Figure 5.  
From Figure 6, we can find that: SQGA converges in 20 generations, accuracy is 99.2, GA converges in the 52 th generation, and accuracy is 98.8. Compared with the other three methods, SQGA has the characteristics of fast convergence and good search results. Next, we compared the results of the proposed method with other single classification methods. The single classifier adopts the base classifier method in this paper: SVM, KNN, BN and BPNN. Both SVM and BPNN select Gaussian function as kernel function and activation function, and the parameters of each learner were determined by the 5-fold cross-validation and grid search methods. The classification results are given in Table 1.  
Figure 5: SQGA optimization results of different datasets. Figure 6: Optimization results from different methods. From the experimental results, we can see that the classification accuracy of KNN is basically the lowest. Compared with other learning methods, the KNN algorithm is simple and efficient but it extracts the least classification information. SVM, BN and BPNN achieve similar classification results. These three methods use different learning mechanisms to learn samples from different angles, and they can better deal with nonlinear classification problems. The proposed method can achieve optimal classification accuracy, which is because the errors and omissions of some individual classifiers can be compensated by other individual classifiers through the ensemble of each classifier. This ensemble learning method can further improve the generalization ability of the overall classification. Then, we analyzed the performance between the proposed method and the traditional ensemble learning algorithm. The traditional ensemble algorithm selects the classical bagging and boosting algorithms, and the number of base classifiers was 40. The differences between base classifiers can reflect the effect of ensemble learning.