Research Article
Novel Methods Generated by Genetic Programming for the Guillotine-Cutting Problem
Table 3
Evaluation of algorithms with instances from group GT2.
| Algorithm | Avg. fitness (%) | Avg. error (%) | Best error (%) | Worst error (%) | SD (%) | Hits | Time (s) |
| A1 | 8.33 | 9.35 | 2.34 | 16.12 | 4.43 | 0 | 21.0 | A2 | 3.53 | 3.97 | 1.47 | 8.14 | 2.20 | 0 | 20.0 | A3 | 6.04 | 6.80 | 3.08 | 16.12 | 3.46 | 0 | 18.0 | A4 | 3.51 | 3.94 | 0.88 | 7.24 | 2.43 | 0 | 17.0 | A5 | 4.43 | 4.95 | 1.41 | 13.89 | 3.54 | 0 | 25.0 | A6 | 5.64 | 6.34 | 1.32 | 18.53 | 4.61 | 0 | 23.0 | A7 | 4.53 | 5.09 | 1.01 | 11.76 | 2.85 | 0 | 15.0 | A8 | 5.64 | 6.34 | 1.32 | 18.53 | 4.61 | 0 | 14.0 | A9 | 6.04 | 6.80 | 3.08 | 16.12 | 3.46 | 0 | 17.0 | A10 | 7.15 | 8.03 | 3.09 | 16.12 | 4.11 | 0 | 15.0 | A11 | 4.29 | 4.83 | 1.32 | 11.76 | 2.94 | 0 | 13.0 | A12 | 4.56 | 5.12 | 1.07 | 18.53 | 4.57 | 0 | 11.0 | A13 | 4.32 | 4.84 | 1.38 | 12.20 | 3.24 | 0 | 19.0 | A14 | 3.61 | 4.06 | 1.59 | 8.14 | 2.08 | 0 | 20.0 | A15 | 3.55 | 4.00 | 1.21 | 12.20 | 3.26 | 0 | 22.0 | A16 | 4.29 | 4.83 | 1.32 | 11.76 | 2.94 | 0 | 27.0 | A17 | 6.00 | 6.73 | 1.64 | 18.50 | 4.71 | 0 | 21.0 | A18 | 6.04 | 6.80 | 3.08 | 16.12 | 3.46 | 0 | 22.0 | A19 | 8.33 | 9.35 | 2.34 | 16.12 | 4.43 | 0 | 27.0 | A20 | 5.73 | 6.46 | 1.64 | 16.12 | 4.03 | 0 | 26.0 | A21 | 6.04 | 6.80 | 3.08 | 16.12 | 3.46 | 0 | 20.0 | A22 | 6.30 | 7.07 | 1.64 | 18.50 | 4.70 | 0 | 19.0 | A23 | 3.89 | 4.38 | 1.83 | 8.97 | 2.18 | 0 | 20.0 | A24 | 6.04 | 6.80 | 3.08 | 16.12 | 3.46 | 0 | 24.0 | A25 | 5.13 | 5.78 | 2.00 | 16.12 | 3.55 | 0 | 15.0 | A26 | 6.06 | 6.81 | 3.08 | 16.12 | 3.38 | 0 | 25.0 | A27 | 6.04 | 6.80 | 3.08 | 16.12 | 3.46 | 0 | 19.0 | A28 | 3.51 | 3.94 | 0.88 | 7.24 | 2.43 | 0 | 17.0 | A29 | 3.53 | 3.97 | 1.47 | 8.14 | 2.20 | 0 | 14.0 | A30 | 5.99 | 6.73 | 1.32 | 18.53 | 4.94 | 0 | 15.0 | Average | 5.27 | 5.92 | 1.90 | 14.20 | 3.50 | 0 | 19.0 |
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