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Table 2 Optimised parameter values for experimental saccadic velocity profiles

From: Optimisation of an exemplar oculomotor model using multi-objective genetic algorithms executed on a GPU-CPU combination

Selection method α β ε γ α β
I 16.4500 (0.6088) 36.1980 (0.4769) 0.0074 (0.0094) 0.000034 (0.4659) 537.6500 (0.0112) 3.3400 (0.0232)
II 264.4100 (0.8873) 7.6514 (1.1634) 0.0038 (0.0255) 0.087352 (0.5497) 249.2100 (0.0052) 0.3900 (0.0942)
III 163.3700 (0.9756) 37.0826 (0.3589) 0.0049 (0.0846) 0.012519 (1.0388) 409.5300 (0.0316) 1.3200 (0.1752)
IV 42.3500 (0.8777) 39.2892 (0.3936) 0.0057 (0.0077) 0.000744 (0.2041) 468.5600 (0.0020) 2.9400 (0.0079)
  1. Results are shown for each method used to select the final solution. Method I selects the solution that minimises the Euclidean
  2. distance between the Pareto front population and the objective space origin; method II selects the best fit to a 5 deg saccade
  3. (objective 1); method III selects the best fit to a 10 deg saccade (objective 2); method IV selects the best fit to a 20 deg saccade
  4. (objective 3). Mean parameter values and coefficients of variation (shown in brackets) were calculated from 16 NSGA-II
  5. runs with a population size of 8000. Optimised parameter values for individual NSGA-II runs are listed in Additional file 1: Tables S8-S11