Research Article

Novel Methods Generated by Genetic Programming for the Guillotine-Cutting Problem

Table 3

Evaluation of algorithms with instances from group GT2.

AlgorithmAvg. fitness (%)Avg. error (%)Best error (%)Worst error (%)SD (%)HitsTime (s)

A18.339.352.3416.124.43021.0
A23.533.971.478.142.20020.0
A36.046.803.0816.123.46018.0
A43.513.940.887.242.43017.0
A54.434.951.4113.893.54025.0
A65.646.341.3218.534.61023.0
A74.535.091.0111.762.85015.0
A85.646.341.3218.534.61014.0
A96.046.803.0816.123.46017.0
A107.158.033.0916.124.11015.0
A114.294.831.3211.762.94013.0
A124.565.121.0718.534.57011.0
A134.324.841.3812.203.24019.0
A143.614.061.598.142.08020.0
A153.554.001.2112.203.26022.0
A164.294.831.3211.762.94027.0
A176.006.731.6418.504.71021.0
A186.046.803.0816.123.46022.0
A198.339.352.3416.124.43027.0
A205.736.461.6416.124.03026.0
A216.046.803.0816.123.46020.0
A226.307.071.6418.504.70019.0
A233.894.381.838.972.18020.0
A246.046.803.0816.123.46024.0
A255.135.782.0016.123.55015.0
A266.066.813.0816.123.38025.0
A276.046.803.0816.123.46019.0
A283.513.940.887.242.43017.0
A293.533.971.478.142.20014.0
A305.996.731.3218.534.94015.0
Average5.275.921.9014.203.50019.0