## 3 Results
### Genetic Algorithm Adjustment  
The differences shown in the maps above suggest that the dataset adjustments after mean correction--particularly in the PEM-1 case--are not well suited for producing an exposure map. For this reason, we analyzed the possibility of finding an adjustment that minimizes the differences between the map obtained by means of the BBM and the corrected PEM and not between the mean values of both sets. The differentiation between LOS and NLOS measurements was maintained in order to increase the degree of freedom of the algorithm. Figure 9 shows the flowchart followed. Each individual in the population consists of two values (k\({}_{1}\) and k\({}_{2}\)), which are the proportionality constants between the LOS and NLOS measurement subsets, respectively. First, the exposure map corresponding to the BBM measurements is obtained. This map is identical to the one in Figure 5. An  
Figure 8: PEM-2 and differences in the PEM-2 vs. BBM exposure maps. initial population of 50 individuals is generated. Their values of k\({}_{1}\) and k\({}_{2}\) are obtained as 1 + N (0, 1), the latter being a normal distribution of mean 0 and standard deviation 1. With these values, an automatic estimation of the semi-variogram is performed with the autofitVariogram function of the R AutoMap library. Based on this analysis, it is possible to determine the relationship between the distance of the samples and the difference in the acquired measurements. Once the semivariogram has been estimated and adjusted, the krige function of the gstat library is used to perform the interpolation with kriging, resulting in a raster layer with the continuous distribution of the variable. This layer is then clipped to the study area. Once all the solutions necessary for the analysis have been extracted, they are normalized in values from 0 to 255. Subsequently, to determine the differences, the Structural Similarity Index of the layers obtained is determined by taking the layer generated with the BBM data as the good image and subtracting those obtained from the genetic solutions. Therefore, the exposure maps of each individual are generated and then subtracted from the BBM map. The sum of the absolute value of the difference at each point on the surface corresponds to the fitness function (absolute value of the difference between both maps). The 25 best values correspond to the individuals that will be parents of the next generation. The offspring are obtained by adding to each parent a random value of mean 0 and decreasing variance (\(\sigma\)) over the following generations. The process is repeated until 50 generations have been completed. Figure 9: Flowchart of the evolutionary algorithm optimization. Evolutionary algorithms are characterized by obtaining a different solution on each run. Successive runs yield a range of sub-optimal solutions of the constants k\({}_{1}\) and k\({}_{2}\). For this reason, the algorithm shown in Figure 9 was run five times, obtaining the best values shown in Table 3.  
The final values of k\({}_{1}\) and k\({}_{2}\) are in the ranges 1.7011-1.4853 and 1.4062-1.2490, respectively. These ranges are consistent with the values obtained from the relationship found in Section 3.1 (1.651 and 1.380, respectively). Using the pair of values corresponding to the first iteration, the exposure map and the difference map shown in Figure 10 were produced. This solution is indicated as PEM-G, since a double correction is made, and the values of which were obtained by means of a genetic algorithm. Although differences are observed in the areas of maximum exposure, the values are lower than in the previous cases. The discussion section contains an analysis of the limitations of this method, which lead to the persistence of differences. Figure 10: PEM-G and difference between the PEM-G and BBM exposure maps.