A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems

Table 4

The fitness values of our algorithm compared against the best fitness values of current approaches.

Data sets	Our result	Carter et al. [8]	Caramia et al. [19]	Di Gaspero and Schaerf [20]	Paquete and Stützle [21]	Burke and Newall [22]	Burke et al. [1]
Car 91	6.4	7.1	6.6	6.2	—	4.65	5.0
Car 92	5.2	6.2	6.0	5.2	—	4.1	4.3
Ear 83	39.8	36.4	29.3	45.2	38.2	37.05	36.2
Hec 92	11.8	10.8	9.2	12.2	11.2	11.54	11.6
Kfu 93	16.2	14.0	13.2	18.2	16.2	13.9	15.0
Lse 91	14.5	10.5	9.6	15.2	13.2	10.82	11.0
Rye 92	12.3	7.3	6.8	—	—	—	—
Sta 83	157.2	161.5	158.2	160.2	168.2	168.73	161.9
Tre 92	9.5	9.6	9.2	10.2	9.2	8.35	8.4
Uta 92	4.3	3.5	3.2	4.2	—	3.2	3.4
Ute 92	28.6	25.8	24.4	27.8	29.2	25.83	27.4
York 83	40.5	41.7	36.2	41.2	38.2	37.28	40.8
Data sets	Burke et al. [23]	Asmuni et al. [24]	Côté et al. [17]	Kendall and Hussin [25]	Yang and Petrovic [26]	Abdullah et al. [27]	Burke et al. [28]
Car 91	4.8	5.19	5.2	5.37	4.5	5.2	5.36
Car 92	4.2	4.51	4.2	4.67	3.93	4.4	4.53
Ear 83	35.4	36.64	34.2	40.18	33.7	34.9	37.92
Hec 92	10.8	11.6	10.2	11.86	10.83	10.3	12.25
Kfu 93	13.7	15.34	14.2	15.84	13.82	13.5	15.2
Lse 91	10.4	11.35	11.2	—	10.35	10.2	11.33
Rye 92	8.9	10.05	8.8	—	8.53	8.7	—
Sta 83	159.1	160.79	157.2	157.38	158.35	159.2	158.19
Tre 92	8.3	8.47	8.2	8.39	7.92	8.4	8.92
Uta 92	3.4	3.52	3.2	—	3.14	3.6	3.88
Ute 92	25.7	27.55	25.2	27.6	25.39	26.0	28.01
York 83	36.7	39.79	36.2	—	36.35	36.2	41.37
Data Sets	Eley [29]	Qu et al. [9]	Pillay and Banzhaf [30]	Burke et al. [31]	Burke et al. [32]	Xu et al. [33]
Car 91	5.2	5.16	4.9	4.6	5.19	6.5
Car 92	4.3	4.16	4.2	4.0	4.31	5.2
Ear 83	36.8	35.86	35.9	32.8	35.79	39.6
Hec 92	11.1	11.94	11.5	10.0	11.19	11.63
Kfu 93	14.5	14.79	14.4	13.0	14.51	16.3
Lse 91	11.3	11.15	10.9	10.0	10.92	13.4
Rye 92	9.8	—	10.89	—	—	14.7
Sta 83	157.3	159.0	157.8	159.9	157.18	160.2
Tre 92	8.6	8.6	8.4	7.9	8.49	9.5
Uta 92	3.5	3.59	3.4	3.2	3.44	3.2
Ute 92	26.4	28.3	27.2	24.8	26.7	27.9
York 83	39.4	41.81	39.3	37.28	39.47	—