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Fig. 5 | BMC Systems Biology

Fig. 5

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

Fig. 5

Convergence metrics of NSGA-II when fitting the model to experimental nystagmus waveform a of Fig. 4. a Mean value of the hypervolume indicator \(\mathcal {H}_{I}\) as a function of generation number n. b Standard deviation (SD) of \(\mathcal {H}_{I}\) as a function of n. c Mean value of the smallest Euclidean distance \(d_{\hat {\mathcal {F}}}\) between the Pareto front estimate and objective space origin as a function of n. d SD of \(d_{\hat {\mathcal {F}}}\) as a function of n. Convergence metrics were calculated from 16 runs of NSGA-II each for the following population sizes: 500, 1000, 2000, 4000 and 8000. The equivalent plots for waveforms b, c and d can be seen in Additional file 1: Figure S17, Additional file 1: Figure S18 and Additional file 1: Figure S19, respectively

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