Benchmarking RCGAu on the Noiseless BBOB Testbed

Table 2

ERT loss ratio versus the budget (both in number of -evaluations divided by dimension). The target value for a given budget FEvals is the best target -value reached within the budget by the given algorithm. Shown is the ERT of the given algorithm divided by best ERT seen in GECCO-BBOB-2009 for the target , or, if the best algorithm reached a better target within the budget, the budget divided by the best ERT. Line: geometric mean. Box-Whisker error bar: 25–75%-ile with median (box), 10–90%-ile (caps), and minimum and maximum ERT loss ratio (points). The vertical line gives the maximal number of function evaluations in a single trial in this function subset. See also Figure 3 for results on each function subgroup.

#FEs/	–  in 5-D, maxFE/ = 100018
	best	10%	25%	med	75%	90%
2	0.62	1.0	1.4	2.5	4.0	8.5
10	1.3	1.6	2.9	3.5	5.8	16
100	2.3	2.6	6.4	8.4	13	42
	4.2	5.0	12	40	62
	13	25	32
	3.4	35	83
	3.4	35
RLUS/
#FEs/	– in 20-D, maxFE/ = 100004
	best	10%	25%	med	75%	90%
2	0.94	1.1	5.8	24	40	40
10	1.3	4.8	7.0	16
100	4.3	6.2	11	21	39
	5.0	7.6	23	48
	39	53
	43
RLUS/