## 4 Experiments
### Benchmark Suite
#### 4.1.2 Experimental Results  
As mentioned before, Galapagos employs GA, SilverEye (v1.1.0) uses PSO and Opossum (v1.5.0) considers RBFOpt for enabling architectural design optimization in the GH environment. We compared results of Optimus, which uses jEDE, with those three optimization tools to present its performance. In addition, all the runs for each component and for each function were taken by the authors. Table 1 shows the fitness results of Optimus, Opossum, SilverEye, and Galapagos together with optimal fitness values of each function. Table 2 clearly indicates the superiority of the Optimus over other optimization tools such that it yielded the lowest minimum function values (\(fx\_min\)) in nineteen (19) functions out of twenty (20) functions, significantly. On the other hand, Galapagos favors Optimus in only one function (\(F_{8}\)) with a very small difference. Maximum (\(fx\_max\)) and average (\(fx\_avg\)) function values in Table 1 further justify the better performance of the Optimus in such a way that the maximum and average function values of Optimus are closer to optimal fitness values in nineteen (19) functions than those yielded by other tools. Furthermore, the average number of function evaluations (\(FES\)) within thirty minutes in each problem were the highest in Optimus. This clearly shows high margins between Optimus and other components where Optimus was tremendously faster in each iteration. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Function** \\ \hline \(F_{split}\) & Sphere Function \\ \(F_{\text{rs}}\) & [PERSON]’s Function \\ \(F_{ack}\) & [PERSON]’s Function \\ \(F_{grw}\) & [PERSON]’s Function \\ \(F_{ns}\) & [PERSON]’s Function \\ \(F_{sch}\) & Generalized Schwefel’s Problem 2.26 \\ \(F_{sal}\) & [PERSON]’s Function \\ \(F_{wht}\) & [PERSON]’s Function \\ \(F_{pr1}\) & Generalized Penalized Function 1 \\ \(F_{pr2}\) & Generalized Penalized Function 2 \\ \(F_{1}\) & Shifted Sphere Function \\ \(F_{2}\) & Shifted Schwefel’s Problem 1.2 \\ \(F_{3}\) & Shifted Rotated High Conditioned Elliptic Function \\ \(F_{4}\) & Shifted Schwefel’s Problem 1.2 With Noise in Fitness \\ \(F_{5}\) & Schwefel’s Problem 2.6 With Global Optimum on Bounds \\ \(F_{6}\) & Shifted [PERSON]’s Function \\ \(F_{7}\) & Shifted Rotated [PERSON]’s Function without Bounds \\ \(F_{8}\) & Shifted Rotated [PERSON]’s Function with Global Optimum on Bounds \\ \(F_{9}\) & Shifted [PERSON]’s Function \\ \(F_{10}\) & Shifted Rotated [PERSON]’s Function \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Benchmark Suite. For example, when solving \(F_{sph}\), the Optimus (jEDE) approximately realized 5844 FES/minute, whereas GA made 1643 FES/minute, PSO made 1205 FES/minute, RBFOpt made 85 FES/minute. These facts explicitly imply the superiority of Optimus over other components in solving benchmark suite. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Optimus\_jEDE** & **Opossum\_RBFOpt** & **SilverEye\_PSO** & **Galapagos\_GA** & **Optimal** \\ \hline \multirow{4}{*}{\(F_{sph}\)} & \(f(x)\_min\) & \(0.0000000\times 10^{0}\) & \(1.4000000\times 10^{-5}\) & \(5.9000000\times 10^{-5}\) & \(1.1709730\times 10^{0}\) & \\  & \(f(x)\_max\) & \(0.0000000\times 10^{0}\) & \(5.8000000\times 10^{-5}\) & \(2.7057298\times 10^{1}\) & \(4.4052130\times 10^{0}\) & \\  & \(f(x)\_avg\) & \(0.0000000\times 10^{0}\) & \(3.6400000\times 10^{-5}\) & \(5.4171618\times 10^{0}\) & \(2.7928586\times 10^{0}\) & 0 \\  & Std.Dev.