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Table 2 Comparison of trained parameters along with their fitness for GA and GA-SQP algorithms in case of problem 1

From: Memetic computing through bio-inspired heuristics integration with sequential quadratic programming for nonlinear systems arising in different physical models

Method Proposed solutions ɛ
t 1 t 2 t 3 t 4
GA-1 −0.999404287 0.999479968 −0.027480501 −1.999042387 1.8262E−08
GA-2 0.979068695 −0.981748424 0.163179035 1.966420060 2.1943E−05
GA-3 0.868284707 0.877541519 0.392426227 −1.784981013 7.0007E−04
GA-4 −0.987183026 −0.987817444 0.124553341 −1.978604103 8.6898E−06
GA-5 −0.990822818 −0.991856983 −0.106387112 1.985169394 4.1780E−06
GA-6 0.999913716 −0.999917219 −0.010038843 1.999856079 3.8862E−10
GA-7 0.966617064 0.970313641 0.198841379 1.946148641 5.2871E−05
GA-8 −0.950190958 0.965065042 −0.277206136 1.925861774 2.0243E−04
GA-9 0.973598309 −0.976106840 0.178440757 1.956961516 3.3514E−05
GA-10 0.999854603 0.999934568 0.017078799 −1.999804148 5.4235E−09
GA-11 −0.812150872 0.832453398 0.475826424 1.699196146 1.2769E−03
GA-12 0.980516502 −0.984157549 −0.162937302 −1.969458167 2.1478E−05
GA-SQP-1 0.999999973 −0.999999974 0.000178418 −1.999999955 3.8017E−17
GA-SQP-2 −0.999999973 −0.999999974 0.000178410 −1.999999955 3.8011E−17
GA-SQP-3 0.999999973 0.999999974 0.000178411 1.999999955 3.8012E−17
GA-SQP-4 −0.999999973 0.999999974 0.000178416 1.999999955 3.8015E−17
GA-SQP-5 −0.999999973 0.999999974 −0.000178417 1.999999955 3.8016E−17
GA-SQP-6 0.999999973 −0.999999974 −0.000178410 1.999999955 3.8011E−17
GA-SQP-7 −0.999999973 0.999999974 −0.000178410 1.999999955 3.8011E−17
GA-SQP-8 0.999999975 −0.999999977 −6.283010586 1.999999959 3.3944E−17
GA-SQP-9 0.999999973 0.999999974 0.000178410 1.999999955 3.8012E−17
GA-SQP-10 −0.999999973 −0.999999974 −0.000178408 1.999999955 3.8010E−17
GA-SQP-11 −0.999999973 0.999999974 0.000178430 1.999999955 3.8025E−17
GA-SQP-12 0.999999973 0.999999974 −0.000178417 −1.999999955 3.8016E−17