# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 6 Results
### Experiment Green AI with CIFAR
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{\(CO_{2}e\) (g/kWh)} & \multicolumn{4}{c}{**Val Accuracy**} \\ \cline{2-10}  & **Std** & **Var** & **Avg** & **Median** & **Std** & **Var** & **Avg** & **Median** \\ \hline \hline ASHA & 0.657 & 0.432 & 0.959 & 0.723 & 9.818 & 96.391 & 71.746 & 75.825 \\ BOHB & 0.512 & 0.263 & 0.809 & 0.753 & 8.457 & 71.528 & 72.924 & 75.915 \\ HB & 0.578 & 0.334 & 0.870 & 0.748 & 9.258 & 85.718 & 71.799 & 74.350 \\ PBT & 0.569 & 0.323 & 0.846 & 0.644 & 6.368 & 40.553 & 73.918 & 75.905 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative performance of algorithms in Green AI with 100 trials. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Validation Accuracy** & \(E_{Total}\) **(w)** & **Time (s)** & **lr** & **Epoch** & **Optimizer** & **Bach** & \(CO_{2}e\) (g/kWh)** \\ \hline ASHA & 57.11 & 5419.03 & 1462.94 & 0.0015 & 20 & SGD & 16 & 1.030 \\ ASHA & 65.64 & 4169.07 & 515.17 & 0.00125 & 5 & AD & 16 & 0.792 \\ ASHA & 79.44 & 5694.30 & 732.91 & 0.001 & 20 & Rprop & 64 & 1.082 \\ ASHA & 80.08 & 5700.88 & 739.54 & 0.0015 & 20 & AD & 64 & 1.083 \\ BOHB & 64.27 & 2360.44 & 446.59 & 0.0015 & 5 & SGD & 16 & 0.448 \\ BOHB & 68.81 & 1112.85 & 182.34 & 0.001 & 5 & SGD & 64 & 0.211 \\ BOHB & 80.34 & 2646.50 & 372.06 & 0.00125 & 10 & AD & 64 & 0.503 \\ BOHB & 81.11 & 4827.45 & 720.88 & 0.001 & 20 & Rprop & 64 & 0.917 \\ HB & 39.53 & 13,085.63 & 1965.14 & 0.0015 & 20 & AD & 16 & 2.486 \\ HB & 65.31 & 851.26 & 147.24 & 0.0015 & 5 & SGD & 64 & 0.162 \\ HB & 78.82 & 7143.64 & 1077.33 & 0.00125 & 20 & AD & 32 & 1.357 \\ HB & 80.23 & 1254.59 & 325.38 & 0.0015 & 10 & Rprop & 64 & 0.238 \\ PBT & 69.01 & 1274.76 & 180.78 & 0.001 & 5 & SGD & 64 & 0.242 \\ PBT & 72.35 & 3199.51 & 480.07 & 0.001 & 5 & Rprop & 16 & 0.608 \\ PBT & 79.58 & 1390.35 & 279.76 & 0.0015 & 10 & AD & 64 & 0.264 \\ PBT & 80.22 & 2295.46 & 358.83 & 0.0015 & 10 & Rprop & 64 & 0.436 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The table elucidates the findings of the experiment conducted within the Green AI. A total of ten trials were executed, showing the selection of the two best and worst results, each comprising five iterations of the ASHA, BOHB, HB, and PBT algorithms, each with distinct hyperparameters. The optimized hyperparameters included learning rates (lrs) of 0.001, 0.00125, and 0.0015; epochs ranging from 5 to 20 iterations; optimizers such as Adam (AD), RMSprop (Rprop), and SGD; and batch sizes of 16, 32, and 64. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{\(CO_{2}e\) (g/kWh)} & \multicolumn{4}{c}{**Val Accuracy**} \\ \cline{2-10}  & **Std** & **Var** & **Avg** & **Median** & **Std** & **Var** & **Avg** & **Median** \\ \hline \hline ASHA & 0.565 & 0.287 & 1.059 & 0.932 & 7.538 & 51.133 & 72.750 & 74.170 \\ BOHB & 0.584 & 0.307 & 0.794 & 0.710 & 5.717 & 29.415 & 74.644 & 75.930 \\ HB & 0.723 & 0.470 & 0.811 & 0.549 & 12.357 & 137.427 & 72.323 & 78.210 \\ PBT & 0.830 & 0.621 & 0.827 & 0.522 & 3.536 & 11.251 & 76.788 & 77.630 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative performance of algorithms in Green AI with 10 trials. \(p=0.514\) were obtained. Both \(p\)-values exceed the conventional threshold of 0.05, indicating that there are no statistically significant differences in the medians of \(CO_{2}e\) and validation accuracy among the evaluated algorithms, suggesting similar distributions for both metrics.