Table 1.

Effect of error on the optimization

Points Before Max	Points Before 95%	Percentage Max Found	Percentage 95% of Max Found	Error
7.67	6.48	100%	100%	0%
57.23	22.66	100%	100%	10%
137.41	57.03	82%	100%	20%

Note: Higher error in evaluation of the objective function significantly impacts the performance of the maximization algorithm. Points Before Max represents the number of evaluations before maximum is reached, on average across 100 simulations, and Points Before 95% represents the average number of evaluations needed before reaching a point that is at least 95% of the maximum. Percentage Max Found and Percentage 95% of Max Found indicate how often, across the 100 simulations, the optimizer found the global maximum or a point that is at least 95% of the maximum. Finding the absolute maximum becomes increasingly difficult as the difference between points gets increasingly less distinguishable with higher error rate. Source data are provided as a Source Data file