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

Table 2

Best algorithm of each of the 30 runs.

Algorithm	Avg. fitness (%)	Avg. error (%)	Best error (%)	Worst error (%)	SD (%)	No. of hits	No. of nodes	Tree height	Time (s)
A1	6.52	7.40	0.00	15.22	5.17	1	13	4	553.00
A2	4.69	5.33	1.36	20.75	5.42	0	13	4	560.82
A3	5.84	6.63	0.00	14.37	4.36	1	13	3	592.03
A4	4.38	4.96	2.02	18.29	4.51	0	13	3	532.11
A5	5.18	5.89	0.86	10.57	3.67	0	13	5	491.15
A6	5.85	6.66	1.36	12.93	3.73	0	13	3	595.45
A7	4.28	4.85	1.36	10.74	2.91	0	13	4	564.03
A8	5.85	6.66	1.36	12.93	3.73	0	13	4	599.26
A9	5.84	6.63	0.00	14.37	4.36	1	13	4	603.54
A10	4.63	5.26	0.00	11.64	3.79	1	13	4	600.85
A11	5.19	5.91	1.36	14.96	3.65	0	13	3	511.67
A12	3.49	3.96	0.00	9.83	2.63	1	13	3	556.78
A13	4.50	5.14	0.00	13.87	3.98	1	13	4	659.11
A14	3.92	4.45	0.75	10.23	3.09	0	13	4	597.02
A15	5.63	6.41	1.31	20.75	6.47	0	13	6	596.93
A16	4.64	5.28	0.75	13.17	3.40	0	13	4	518.51
A17	6.15	6.98	0.86	17.77	5.19	0	13	3	607.99
A18	5.84	6.63	0.00	14.37	4.36	1	13	3	607.98
A19	6.34	7.20	0.00	14.82	4.62	1	13	4	591.04
A20	5.03	5.70	0.00	12.52	4.02	1	13	5	618.95
A21	5.84	6.63	0.00	14.37	4.36	1	13	4	596.01
A22	5.59	6.37	1.36	13.14	4.16	0	13	5	592.75
A23	5.09	5.81	0.00	17.95	4.85	1	13	4	598.61
A24	5.84	6.63	0.00	14.37	4.36	1	13	4	580.30
A25	4.23	4.80	0.00	14.48	4.26	1	13	5	648.99
A26	5.15	5.86	0.00	17.95	4.62	1	13	4	516.53
A27	5.84	6.63	0.00	14.37	4.36	1	13	4	602.66
A28	4.38	4.96	2.02	18.29	4.51	0	13	4	587.45
A29	4.69	5.33	1.36	20.75	5.42	0	13	5	549.60
A30	6.25	7.11	0.00	15.81	4.82	1	13	4	573.66
Average	5.22	5.93	0.60	14.85	4.29	0.5	13	4	580.16