Using Different Approaches to Approximate a Pareto Front for a Multiobjective Evolutionary Algorithm: Optimal Thinning Regimes for Eucalyptus fastigata

Table 1

Parameters of the state equations (7)–(9) [2].

Parameter	Value	95% confidence bounds
		Lower bound	Upper bound
𝑎_1	0.1467	0.1088	0.1847
𝑏_1	−000337	−0.005411	−0.001328
𝑐_1	1.871	1.844	1.898
𝑑_1	−4.817𝑒−06	−1.013𝑒−05	4.922𝑒−07
𝑎_2	−0.1428	−0.1788	−0.1069
𝑏_2	−0.00358	−0.005614	−0.001547
𝑐_2	−0.8829	−0.9068	−0.859
𝑑_2	−8.491𝑒−06	−1.852𝑒−05	1.536𝑒−06
𝑎_3	0.001906	0.0001262	0.003685
𝑏_3	0.8226	0.7127	0.9326
𝑐_3	0.1109	0.03341	0.1885
𝑎_4	−1.055	−1.135	−0.9745
𝑐_4	0.9548	0.9056	1.004
𝑎_5	−7510	−1.051𝑒+04	−4508
𝑐_5	1.13𝑒+04	6447	1.615𝑒+04
𝑎_6	0.1103	0.07602	0.1446
𝑏_4	−0.001873	−0.003263	−0.000483
𝑐_6	1.973	1.933	2.013
𝑑_3	−7.167𝑒−06	−1.331𝑒−05	−1.02𝑒−06
𝑎_7	−0.1119	−0.1447	−0.07914
𝑏_5	−0.001911	−0.00325	−0.0005712
𝑐_7	−0.9798	−1.018	−0.9417
𝑑_4	−1.365𝑒−05	−2.558𝑒−05	−1.725𝑒−06
𝑎_8	0.004516	−0.002179	0.01121
𝑏_6	0.8976	0.722	1.073
𝑐_8	0.5459	0.07415	1.018