Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms

Table 5

Statistics of the fine-tuning approaches for the metaheuristic GA.

Inst.
Berlin52	44.60	109	34.83	52
St70	72.15	103	74.67	69
Rat99	106.69	123	106.69	82
Rd100	116.94	114	114.80	74
Lin105	143.98	95	150.88	61
Pr124	194.38	110	205.06	60
Bier127	99.38	100	88.84	63
Ch130	142.36	125	130.70	103
KroA200	227.62	125	216.68	103
Ts225	245.84	112	282.68	74
	139.39	109	140.58	52
	62.33	11	70.97	17