## 4 Discussion
The results in the measurement of the parameters via CV can be seen in the correlation matrix of Figure 11. It can be seen that the biometric parameter with the best correlation to the BW was RHK with 0.80. With all the parameters in this range [0.7 to 0.8], each biometric parameter used correlated highly with the BW. The real parameters and those obtained with the Kinect10 sensor showed a minimum value of 0.83 and a maximum value of 0.93 for ASC and HW, respectively. These values might indicate that the measured values with respect to the real value have an average error of less than 10%. However, there were some atypical observations due to disturbances, such as sudden movements of the animals during the measurements. This situation caused some values, such as ASC in Sheep 31 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Equation** & **References** \\ \hline [PERSON]’s formula a & \(W=(Girth\times length)/Y\) & [76] \\ [PERSON]’s formula b & \(W=(L\times G^{2})/300\) & [76] \\ Logistic model c & \(Y=A(1+e^{-Kt})^{-M}\) & [77] \\ [PERSON] c & \(Y=Aexp(-Be^{-kt})\) & [78] \\ [PERSON] & \(Y=A(1-Be^{-kt})^{3}\) & [77] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mathematical modelsand 32, to have atypical measurement errors: 50% and 52%, respectively. This error was reflected in the weight prediction by the algorithm, obtaining errors for those points of 25.7% and 27.7%. These point errors for Sheep 31 and 32 were still lower than the average reported, for example, by the Schaefferformula for Jersey Cross cattle body weight (kg), which was around 31%. Table 5 shows the statistical indicators obtained by each algorithm. The performance of each algorithm was measured with the RMSE, defined by Equation (2). The GA obtained an RMSE of 7.68%, and the CSA obtained an RMSE of 7.55%. The difference between both algorithms was 0.13%. Regarding the correlation coefficient \(R^{2}\), the value for GA was 79.23% and 79.98%. This is a difference of only 0.75%. Although each algorithm found polynomials with a similar performance reflected in the RMSE and \(R^{2}\), each algorithm found a different local optimum. The objective of using two different metaheuristic algorithms was to compare other solutions with similar performance. Additionally, it was observed that, at the end of the process, neither algorithm presented a reduction of the RMSE. Thus, it is suggested for future or similar works to reduce the number of fitness evaluations. The previous point was demonstrated by the results of both algorithms since the local optimum found by each one reflected a similar performance; however, it is observed in Table 4 that the parameters of each polynomial were different. Figure 12 demonstrates the performance of each algorithm in the search for the solution with the best fitness. While the GA converged to its optimum in fewer fitness evaluations, the CSA had a slightly better optimum. It can be summarized that the local optima for both algorithms were similar in performance. Finally, Figure 13 shows the performance as a weight estimator of each algorithm. Both algorithms presented similar prediction trends. The sheep with ID 31 and 32 presented the largest errors in both algorithms. If they were considered atypical points, the accuracy of the algorithms would increase.