## 3 Results and Discussion  
Figure 4 details the result of 7000 iterations of the algorithm detailed above, with visualization using the Mayavi 3D visualization library for the Python programming language [42]. Each modeled point source on the shell of the shelter object is represented as virtual cubes, with edge lengths of 1 m. The color of the cube displays the activity value in the range from blue (low) to red (high). The \"virtual detectors\" are represented in the form of spheres, each with a conventional diameter of 0.75 m, wherein the colour of the sphere is also coded, in the range from blue to red, with increasing dose-rate. The quality of the implementation of the proposed algorithm was assessed by comparing both individual values of the activities \(S_{j}\), measured by the sensor in the coordinates \((x,y)\) and activity evaluations \(S_{j}\), obtained with the algorithm, as well as arrays of measurements and their estimates. The following were used as quality measures for the arrays of activity values: approximation error \(\sigma_{approx}\) (4), Mean Absolute Percentage Error (MAPE) (5), approximation reliability \(R_{S_{j},\hat{S_{j}}}^{2}\) (6):  
Figure 3: DRAKON visual programming language notation of the proposed evolutionary algorithm. \[\sigma_{aprox}=\sqrt{\frac{\sum_{j=1}^{n}\left(S_{j}-\hat{S_{j}}\right)^{2}}{\left(n -m\right)}} \tag{4}\]  
where \(n\) is the volume of the array under study and m is the number of parameters in the model (1),  
\[MAPE=\frac{1}{n}\sum_{j=1}^{n}\left|\frac{S_{j}-\hat{S_{j}}}{S_{j}}\right|\times 1 00\% \tag{5}\]  
\[R_{S_{j},\hat{S_{j}}}^{2}=\frac{\sum_{j=1}^{n}\left(S_{j}-\hat{S_{j}}\right)^{2 }}{\sum_{j=1}^{n}\left(S_{j}-\overline{S_{j}}\right)^{2}} \tag{6}\]  
where \(\overline{S_{j}}=\frac{1}{n}\sum_{j=1}^{n}S_{j}\), arithmetic average of the activity values measured by the sensor. While the authors note that other differing and complementary error quantifications such as Mean Absolute Error (MAE), Root-Mean-Square Deviation (RMSE), and, especially, Mean Relative Error (MRE) could be used, this work proceeded by assessing the quality of the model and the simulation results, considering the structure of the model over the entire range of values of the measured quantities and iterations. Hence, MAE was not used since it is a linear estimate whereby all errors are weighted equally on average. Furthermore, since no anomalous values of simulation results were observed during the simulation, even on a small number of iterations, the RMSE metric, which can be considered sensitive to anomalous values (large errors have a disproportionately large effect on the RMSE), was also not used. In the case of MRE, this measure shows the  
Figure 4: Result of 7000 iterations of the evolutional algorithm, yielding a color-scaled map of the radiation levels on the roof of the shelter object. magnitude of the absolute error relative to the actual value of the output variable (hence, this error is often referred to as the average relative absolute error, or MRAE) and can show significant variation with input variable magnitude. In contrast, the MAPE error is easy to interpret and has a scale for evaluating the quality of simulation results. The dependence of the approximation absolute error on the number of iterations of the algorithm are shown in Figure 5, with this approximation error used not only as a measure of model quality, but also to control the number of iterations, reaching the minimum error value indicating the end of the algorithm. An R\({}^{2}\) error can also be used to evaluate the quality of a regression model. However, in this instance, error values are not considered, rather the value that depicts how much better the given model works than the model where only the constant is present, while the input variables are absent or the regression coefficients with them are equal to zero. In this work, the R\({}^{2}\) value shows the proportion of variance of the dependent variable explained by the regression model.