# An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem
## 4 Computational Experiments
### Computational Experiment for Non-Zero-Waste Instances
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multirow{2}{*}{\(n\)} & \multirow{2}{*}{**W**} & \multirow{2}{*}{**H**} & \multirow{2}{*}{**Opt.**} & \multicolumn{2}{c}{**AGA [26]**} & \multicolumn{2}{c}{**AGA [29]**} & \multicolumn{2}{c}{**HAGA**} \\ \cline{5-10}  & & & & & \multicolumn{2}{c}{**F/\%**} & \multicolumn{2}{c}{**Time/s**} & \multicolumn{1}{c}{**F/\%**} & \multicolumn{1}{c}{**Time/s**} & \multicolumn{1}{c}{**F/\%**} & \multicolumn{1}{c}{**Time/s**} \\ \hline cgcut1 & 16 & 10 & 23 & 97.8261 & 97.8261 & 2.4 & 97.8261 & 2.23 & 97.8261 & 1.31 \\ cgcut2 & 23 & 70 & 63 & 98.5034 & 98.5034 & 6.33 & 98.5034 & 7.19 & 98.5034 & 4.17 \\ cgcut3 & 62 & 70 & 636 & 99.9551 & 95.9931 & 291.99 & 95.5034 & 297.26 & 96.3971 & 293.18 \\ gcut1 & 10 & 250 & 1016 & 64.3945 & 64.3945 & 1.09 & 64.3945 & 0.94 & 64.3945 & 0.27 \\ gcut2 & 20 & 250 & 1133 & 96.9331 & 90.1407 & 78.9 & 91.7602 & 74.17 & 91.5223 & 72.21 \\ gcut3 & 30 & 250 & 1803 & 90.4384 & 90.4384 & 117.58 & 90.4384 & 119.25 & 90.4384 & 111.95 \\ gcut4 & 50 & 250 & 2934 & 97.9148 & 93.675 & 217.78 & 94.1787 & 220.15 & 94.8336 & 212.34 \\ gcut5 & 10 & 500 & 1172 & 93.0546 & 85.7871 & 33.64 & 87.1279 & 36.93 & 84.3447 & 33.86 \\ gcut6 & 20 & 500 & 2514 & 98.0157 & 93.039 & 70.29 & 93.5469 & 71.78 & 94.3003 & 69.82 \\ gcut7 & 30 & 500 & 4641 & 86.3948 & 84.1737 & 117.04 & 84.7101 & 119.29 & 84.5164 & 119.01 \\ gcut8 & 50 & 500 & 5703 & 98.3855 & 94.121 & 225.67 & 94.4197 & 219.42 & 94.7485 & 215.17 \\ gcut9 & 10 & 1000 & 2022 & 99.9916 & 95.1696 & 33.89 & 94.2799 & 41.33 & 92.6305 & 34.77 \\ gcut10 & 20 & 1000 & 5356 & 99.9884 & 91.2531 & 73.14 & 91.9732 & 77.88 & 91.6382 & 71.28 \\ gcut11 & 30 & 1000 & 6537 & 99.9927 & 94.7406 & 112.19 & 93.6346 & 116.25 & 93.9769 & 113.58 \\ gcut12 & 50 & 1000 & 12522 & 99.9999 & 92.012 & 216.88 & 92.3138 & 210.78 & 91.4614 & 227.23 \\ gcut13 & 32 & 1000 & 4772 & 99.9966 & 94.5067 & 143.68 & 94.2138 & 150.77 & 96.1827 & 127.13 \\ \hline ngcut1 & 10 & 10 & 23 & 82.6087 & 82.6087 & 1.18 & 82.6087 & 0.87 & 82.6087 & 0.26 \\ ngcut2 & 17 & 10 & 30 & 92.3333 & 92.3333 & 3.89 & 92.3333 & 3.27 & 92.3333 & 2.34 \\ ngcut3 & 21 & 10 & 28 & 98.9286 & 98.9286 & 4.25 & 98.9286 & 4.66 & 98.9286 & 2.99 \\ ngcut4 & 7 & 10 & 20 & 81.0000 & 81.0000 & 0.69 & 81.0000 & 0.5 & 81.0000 & 0.16 \\ ngcut5 & 14 & 10 & 36 & 98.0556 & 98.0556 & 48.57 & 98.0556 & 50.41 & 98.0556 & 45.2 \\ ngcut6 & 15 & 10 & 29 & 100.000 & 93.7034 & 61.22 & 94.3448 & 53.94 & 94.8276 & 53.59 \\ ngcut7 & 8 & 20 & 20 & 43.7500 & 43.7500 & 0.39 & 43.7500 & 0.34 & 43.7500 & 0.2 \\ ngcut8 & 13 & 20 & 32 & 98.9063 & 98.9063 & 45.8 & 98.9063 & 43.32 & 89.9063 & 5.97 \\ ngcut9 & 18 & 20 & 49 & 99.3878 & 95.9147 & 61.62 & 96.1224 & 63.4 & 97.3061 & 31.49 \\ ngcut10 & 13 & 30 & 80 & 71.6667 & 71.6667 & 0.64 & 71.6667 & 0.6 & 71.6667 & 0.29 \\ ngcut11 & 15 & 30 & 50 & 98.8667 & 92.3333 & 51.73 & 91.0667 & 46.99 & 91.3067 & 37.85 \\ ngcut12 & 22 & 30 & 87 & 87.9693 & 85.6705 & 81.68 & 87.4866 & 76.49 & 87.9693 & 40.19 \\ \hline beng1 & 20 & 25 & 30 & 98.8000 & 98.8000 & 4.83 & 98.8000 & 4.67 & 98.8000 & 3.19 \\ beng2 & 40 & 25 & 57 & 99.6491 & 99.6491 & 21.58 & 99.6491 & 22.49 & 99.6491 & 16.42 \\ beng3 & 60 & 25 & 84 & 99.5238 & 95.238 & 84.97 & 99.5238 & 73.34 & 99.5238 & 49.58 \\ beng4 & 80 & 25 & 107 & 99.9252 & 99.9252 & 30.63 & 99.9252 & 34.04 & 99.9252 & 20.39 \\ beng5 & 100 & 25 & 134 & 99.4030 & 99.4030 & 196.13 & 99.0430 & 228.56 & 99.4030 & 116.13 \\ beng6 & 40 & 40 & 36 & 98.6111 & 98.6111 & 10.84 & 98.6111 & 14.92 & 98.6111 & 12.6 \\ beng7 & 80 & 40 & 67 & 99.7388 & 99.7388 & 53.03 & 99.7388 & 42.23 & 99.7388 & 24.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the filling rate (%) and calculation time (s) on the non-zero-waste instances.