3.3.5. Comparison with Other ML Algorithms

To better demonstrate the GA-ANN learning approach, the common methods (ANN, SVM and RF) that are often used are also applied in this study. Support vector machines (SVMs) are well-known supervised machine learning techniques that were proposed by Cortes and Vapnik [73] to solve classification problems, and then were extended to regression domain by Vapnik et al. [74]. For nonlinear problems, a nonlinear kernel function is utilized. Random forest (RF) is an effective machine learning method proposed in 2001 [75], which can be applied to classification, regression, and feature selection problems. RF is an ensemble learning model with a decision tree as the base classifier, combining bagging and random subspace theory.

To evaluate the performance of the suggested models, three different metrics, Mean square error (MSE), Correlation coefficient (R), and root mean square error equation (RMSE) are introduced. These statistical indicators assess the efficiency, linear relationship, and deviation experienced from the average values. Statistical indices including MSE, R, and RMSE gave an overall view of the precision and error of the model. The performance measurements of the models are shown in Table 5. GA-ANN model was superior, followed by ANN model, RF model and the least was SVM model. This might be indicated that, ANN, SVM, and RF are individual learning algorithms while GA-ANN is an optimized learning algorithm.

**Table 5.** The performance evaluation of the sugges<sup>t</sup> models.


PSD = predicted simulated defects; PC = predicted coordinates.
