# Evolutionary System Design with Answer Set Programming
## 6 Experiments
### Experimental Evaluation
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \(e\)D & & \(HD\times e\)D \\
**C** & **AVG-R** & **C** & **AVG-R** \\ \hline XYZ-BR-H1-L & 3.2 & BOU-BR-H1-L & 6.9 \\ XYZ-B-H1-L & 3.6 & BOU-BR-H2-L & 8.2 \\ XYZ-B-H2-L & 3.6 & BOU-BR-H1-F-8 & 14.2 \\ XYZ-BR-H1-F-8 & 3.8 & XYZ-B-H2-L & 17.6 \\ XYZ-BR-H1-F-2 & 4.9 & XYZ-B-H1-L & 17.6 \\ XYZ-BR-H2-L & 4.9 & BOU-BR-H2-F-8 & 18.1 \\ XYZ-BR-H2-F-8 & 5.9 & BOU-BR-H1-F-2 & 18.3 \\ XYZ-BR-H2-F-4 & 6.6 & BOU-BR-H1-F-4 & 18.5 \\ XYZ-BR-H1-F-4 & 7.2 & BOU-BR-H2-F-4 & 19.2 \\ XYZ-BR-H2 & 8.4 & **XYZ-B-S2** & 19.5 \\ XYZ-B-H1 & 8.8 & XYZ-BR-H1-L & 19.6 \\ XYZ-B-H2-F-4 & 8.8 & XYZ-BR-H2-L & 20.2 \\ XYZ-B-H2 & 8.8 & **XYZ-BR-H1**-F-8 & 21.2 \\ XYZ-B-H1-F-4 & 8.9 & BOU-BR-H2-F-2 & 23.9 \\ BOU-BR-H1-L & 9.0 & **XYZ-BR-H2**-F-8 & 24.8 \\ XYZ & 9.3 & BOU-BR-H1 & 25.7 \\ BOU-BR-H2-L & 9.5 & **XYZ-BR-H1**-F-2 & 25.9 \\ XYZ-BR-H2-F-2 & 10.4 & BOU-B-H1-F-2 & 26.0 \\ XYZ-B-H1-F-8 & 12.1 & BOU-B-H2-F-2 & 26.0 \\ XYZ-B-H2-F-8 & 12.1 & **XYZ-BR-H1**-F-4 & 26.0 \\ XYZ-BR-H1 & 12.2 & **XYZ-BR-H2**-F-4 & 26.3 \\ XYZ-B-H1-F-2 & 12.6 & **BOU-B-H2**-F-4 & 26.8 \\ XYZ-B-H2-F-2 & 12.6 & **BOU-B-H1**-F-4 & 26.8 \\ XYZ-B-p2 & 12.8 & **XYZ-B-H2**-F-4 & 26.8 \\ BOU-B-H1-F-4 & 17.6 & **BOU-BR-H2** & 27.1 \\ BOU-B-H2-F-4 & 17.6 & **XYZ-B-H1**-F-4 & 27.4 \\
**XYZ-B-S2** & 18.8 & **XYZ-BR-H2** & 27.7 \\ BOU-BR-H1-F-8 & 19.5 & **BOU-B-H1**-L & 27.9 \\ BOU & 20.0 & **BOU-B-H2**-L & 27.9 \\ BOU-BR-H1-F-4 & 21.0 & **XYZ-B-H1**-F-8 & 28.2 \\ BOU-B-H1-F-2 & 21.1 & **XYZ-B-H2**-F-8 & 28.2 \\ BOU-B-H2-F-2 & 21.1 & **AB-BR-H2**-L & 28.3 \\ BOU-BR-H2-F-8 & 21.4 & **ARB-BR-H1**-L & 28.5 \\ BOU-BR-H1-F-2 & 22.3 & **BOU-B-H1**-F-8 & 28.6 \\ BOU-BR-H1 & 22.5 & **BOU-B-H2**-F-8 & 28.6 \\ BOU-BR-H2-F-4 & 23.7 & **XYZ-BR-H1** & 29.2 \\ BOU-B-H1-F-8 & 24.4 & **BOU-B-S2** & 29.4 \\ BOU-B-H2-F-8 & 24.4 & **XYZ-BR-H2**-F-2 & 29.9 \\ BOU-BR-H2 & 25.3 & **XYZ-B-H1**-F-2 & 30.5 \\ BOU-B-H1 & 25.9 & **XYZ-B-H2**-F-2 & 30.5 \\ BOU-B-H2 & 25.9 & **BOU-B-H1** & 32.6 \\ BOU-B-H1-L & 26.2 & **BOU-B-H2** & 32.6 \\ BOU-B-H2-L & 26.2 & **XYZ-B-H2** & 33.8 \\ XYZ-B-p1 & 26.8 & **XYZ-B-H1** & 33.8 \\ BOU-BR-H2-F-2 & 28.0 & **BOU** & 40.8 \\ ARB-BR-H1-L & 29.1 & **XYZ** & 41.6 \\ XYZ-BR-P2 & 29.3 & **ARB-BR-H1**-F-8 & 42.9 \\ XYZ-BR-P1 & 29.5 & **XYZ-B-p2** & 43.5 \\
**BOU-B-S2** & 30.5 & **BOU-B-p2** & 44.0 \\ ARB-BR-H2-L & 31.4 & **ARB-BR-H2**-F-8 & 44.3 \\ ARB & 49.5 & **ARB** & 60.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average rank regarding \(\epsilon\)-dominance and the product with average Hamming distance after cutoff time with instances where strategies were applicable. Successful strategies are highlighted in bold. In conclusion, we were able to achieve our goal of finding similar solutions of good quality compared with design space exploration from scratch by using advanced routing techniques plus heuristics. The most successful configurations not only steer the solving to similar solutions but emphasize deciding similarity atoms first. The heuristic level modifier consistently performs well, followed by the factor modifier; meanwhile, different factor values did not have a significant impact. Strategies are applicable in limited capacity. For about half of the instance set, strategy S2 for binding could only obtain answers that performed well above the baselines and in some cases in the top 10. While the limitations are obvious, recall that strategies give guaranteed similarity in contrast to heuristics. Finally, preferences failed across the board. The more complex optimization led to less solutions found, and treating similarity on the same level as the other optimization criteria might lead to undesired results. One might try hierarchical Pareto optimization, but as of yet, our system is not capable of that.