# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 6 Experimental Results and Discussion
### Generalizability of Evolved Solutions  
Previous subsections have demonstrated that \(Best_{2h}\) outperforms other solutions when there are 200 users and 156 UAVs. However, the number of users and UAVs can vary depending on situations; thus, in order to find the robustness and generalizability of evolved solutions in different environments, the same set of experiments was conducted using the same evolved solutions (\(Best_{1h}\), \(Best_{2h}\)) but with varied numbers of users, UAVs, and distributions of users. We changed the number of users and UAVs by 25% at a time (200, 150, 100, and 50 users and 156, 117, and 78 UAVs) and recorded the fitness of each unique pair of UAVs and users. Tables 3 and 4 compare the fitness obtained with varied numbers of UAVs and users. Based on Table 3, on average, over four scenarios, the performance of \(Best_{2h}\) is between 17.11-45.94% larger than \(Best_{1h}\) and ATRI. Similarly, according to Table 4,  
Figure 9: (a–c) show networks on \(T_{r1}\) using ATRI, GANet 1-hop, GANet 2-hop. GANet obtains better performance by focusing more on areas with users and avoiding areas with no users. the \(Best_{2h}\) fitness is 32.75% to 121.73% higher than ATRI, and between 14.46-27.27% more than \(Best_{1h}\). We also observed a similar performance improvement pattern between \(Best_{1h}\) and ATRI, where \(Best_{1h}\)'s fitness is higher by 14.06% to 73.91%. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Users** & **Methods** & \(\mathbf{T_{1}}\) & \(\mathbf{T_{2}}\) & \(\mathbf{T_{3}}\) & \(\mathbf{T_{4}}\) & \(\mathbf{T_{1}}\) & \(\mathbf{T_{2}}\) & \(\mathbf{T_{3}}\) & \(\mathbf{T_{4}}\) & \(\mathbf{T_{1}}\) & \(\mathbf{T_{2}}\) & \(\mathbf{T_{3}}\) & \(\mathbf{T_{4}}\) \\ \hline \multicolumn{11}{c}{**156 UAVs**} & \multicolumn{8}{c}{**117 UAVs**} & \multicolumn{8}{c}{**78 UAVs**} \\ \hline \multirow{3}{*}{150} & ATRI & 1.52 & 1.11 & 1.29 & 1.43 & 1.38 & 1.09 & 1.28 & 1.33 & 1.20 & 0.71 & 1.14 & 1.28 \\  & GANet-1 hop & 1.58 & 1.44 & 1.53 & 1.62 & 1.52 & 1.52 & 1.62 & 1.71 & 1.36 & 1.38 & 0.93 & 1.60 \\  & GANet-2 hop & **1.97** & **1.96** & **1.95** & **1.93** & **1.69** & **1.88** & **1.80** & **1.91** & **1.63** & **1.66** & **1.64** & **1.66** \\ \hline \multirow{3}{*}{100} & ATRI & 1.34 & 1.13 & 1.24 & 1.42 & 1.26 & 1.40 & 1.28 & 1.34 & 1.10 & 0.81 & 0.93 & 1.23 \\  & GANet-1 hop & 1.58 & 1.29 & 1.39 & 1.55 & 1.42 & 1.33 & 1.48 & 1.57 & 1.24 & 1.31 & 1.50 & 1.58 \\ \cline{1-1}  & GANet-2 hop & **1.84** & **1.98** & **1.93** & **1.98** & **1.78** & **1.86** & **1.78** & **1.91** & **1.61** & **1.49** & **1.66** & **1.78** \\ \hline \multirow{3}{*}{50} & ATRI & 1.18 & 1.13 & 1.16 & 1.20 & 1.05 & 1.06 & 1.12 & 1.06 & 0.81 & 0.37 & 0.71 & 0.84 \\  & GANet-1 hop & 1.23 & 1.20 & 1.29 & 1.38 & 1.22 & 1.27 & 1.36 & 1.25 & 1.08 & 1.34 & 1.27 & 1.15 \\ \cline{1-1}  & GANet-2 hop & **1.32** & **1.92** & **1.29** & **1.64** & **1.51** & **1.77** & **1.85** & **1.60** & **1.21** & **1.70** & **1.67** & **1.55** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing fitness with different numbers of UAVs and users on training scenarios. Figure 10: Network deployments of \(Best_{1h}\) and ATRI on \(T_{2}\) and \(Tr_{3}\). In both scenarios, \(Best_{1h}\) performed better than ATRI because GANet allows UAVs to move toward users with bandwidth demand. These tables show that with changes in either the number of users or both the number of UAVs and users, the \(Best_{2h}\) performance is still the best, indicating that evolved solutions are robust and generalizable. Figure 11 shows the distribution of fitness for each unique pair of users and UAVs using ATRI, \(Best_{1h}\), and \(Best_{2h}\) on the training scenarios. Here, \(Best_{2h}\) performed statistically significantly better than both \(Best_{1h}\) and ATRI with \(p\)-value \(<\) 0.05. This provides evidence that, even with a different numbers of users and UAVs, GANet works well in training scenarios.