# Automated Design of Salient Object Detection Algorithms with Brain Programming
## 4 Experiments and Results
### Experiments with [PERSON]'s Fitness Function on GBVSBP
Therefore, we obtain 30 executions of our system and report the standard deviation for each run set. The experiment with our second model GBVSBP+MCG and the FT database shows outstanding results compared to the previous model, see Table 9. Another remarkable difference is the stability during training regarding the standard deviation, whose performance decreases while testing the best models, where four values score above \(\sigma=3\). Meanwhile, in the testing stage, the best individual achieves 95.06% in the second run. On average, the algorithm discovers the best individuals considering all folds in the first \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \multirow{2}{*}{**Fold**} & \multirow{2}{*}{**Run 1**} & \multirow{2}{*}{**Run 2**} & \multirow{2}{*}{**Run 3**} & \multirow{2}{*}{**Run 4**} & \multirow{2}{*}{**Run 5**} & \multirow{2}{*}{**Run 6**} & \multirow{2}{*}{**Run 6**} \\ \hline  & Trmg & Test & Trmg & Test & Trmg & Test & Trmg & Test & Trmg & Test & Trmg & Test \\
1 & 68.30 & 66.88 & 67.62 & 65.29 & 68.86 & 64.28 & **72.49** & 68.66 & 66.23 & 65.45 & 59.24 & 62.2 \\
2 & 65.23 & 64.06 & 67.10 & 64.76 & 66.79 & 64.82 & 64.18 & 70.12 & 69.25 & 69.69 & 66.26 & 64.96 \\
3 & 68.86 & 66.25 & 64.99 & 62.73 & 60.84 & 61.82 & 64.39 & 65.26 & 68.22 & 63.93 & 66.7 & 62.42 \\
4 & 62.72 & 70.91 & **57.37** & 61.27 & 68.42 & 67.16 & 66.52 & 66.87 & 68.52 & 67.17 & 61.87 & 64.56 \\
5 & 70.74 & 67.10 & 68.99 & 65.43 & 66.26 & 60.37 & 62.86 & 62.73 & 70.36 & 66.6 & 58.94 & 61.45 \\ Average & 67.17 & **67.04** & 65.21 & 63.90 & 66.23 & 63.69 & 66.09 & 66.73 & **68.52** & 66.57 & **62.60** & 63.12 \\ \(\sigma\) & 3.18 & **2.48** & 4.61 & 1.82 & 3.20 & 2.65 & 3.81 & 2.89 & **1.52** & 2.14 & **3.72** & 1.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Program structure of the operators corresponding to the best solution for the IMGSAL database. We select this solution from the 30 executions of Table 5. The final solution applies filters to selected color images and derivatives to the conspicuity maps. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & \\ \multirow{2}{*}{**Fold**} & \multirow{2}{*}{**Run 1**} & \multirow{2}{*}{**Run 2**} & \multirow{2}{*}{**Run 3**} & \multirow{2}{*}{**Run 4**} & \multirow{2}{*}{**Run 5**} & \multirow{2}{*}{**Run 6**} \\ \hline  & Trmg & Test & Trmg & Test & Trmg & Test & Trmg & Test & Trmg & Test & Trmg & Test \\
1 & 63.58 & 62.31 & 62.92 & 61.83 & 65.19 & 65.33 & 62.27 & 61.34 & 62.84 & 63.12 & 63.79 & 62.25 \\
2 & 61.53 & 58.92 & 64.70 & 57.51 & 63.02 & 56.88 & 64.64 & 59.80 & 65.29 & 58.81 & 64.50 & 58.57 \\
3 & 61.58 & 64.61 & 63.47 & 65.46 & 62.18 & 64.13 & 63.71 & 65.22 & 63.20 & 67.66 & 59.71 & 64.90 \\
4 & 59.60 & 62.82 & 58.20 & 59.77 & 59.23 & 60.87 & 60.45 & 63.68 & 59.72 & 61.78 & 59.84 & 60.63 \\
5 & 63.20 & 65.81 & 64.46 & 63.25 & 63.90 & 63.47 & 66.03 & 64.55 & 66.22 & 66.79 & **66.39** & 66.78 \\ Average & 61.90 & 62.89 & 62.75 & 61.56 & 62.70 & 62.14 & 63.42 & 62.92 & **63.45** & **63.63** & 62.85 & 62.83 \\ \(\sigma\) & **1.58** & 2.63 & 2.64 & 3.07 & 2.24 & 3.36 & 2.15 & **2.28** & 2.52 & 3.64 & 2.96 & 2.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of the best individuals of the GBVSBP model for all PASCAL-S database runs. Each pair of training and testing corresponds to one experiment of the k-fold. Therefore, we obtain 30 executions of our system and report the standard deviation for each run set.