BMC Systems Biology

Table 1 Results obtained by the proposed algorithms with different settings for dataset 1 to 4

From: Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

		GA-PSO	iGA-PSO (i = 2)		iGA-PSO (i = 4)
	Migration ( m)	-	m = 10	m = 20	m = 10	m = 20
25 genes (dataset 1)	Avg	0.1437	0.1312	0.1246	0.1195	0.1240
	Best	0.1256	0.1117	0.1113	0.1026	0.1083
	Worst	0.1678	0.1516	0.1429	0.1311	0.1460
	SD	0.0149	0.0103	0.0085	0.0107	0.0093
50 genes (dataset 2)	Avg	0.2288	0.2031	0.2098	0.1944	0.2002
	Best	0.1813	0.1572	0.1606	0.1519	0.1515
	Worst	0.2516	0.2305	0.2267	0.2158	0.2254
	SD	0.0187	0.0174	0.0156	0.0147	0.0180
100 genes (dataset 3)	Avg	0.3947	0.3759	0.3765	0.3656	0.3711
	Best	0.3570	0.3304	0.3501	0.3337	0.3411
	Worst	0.4571	0.4067	0.4140	0.3957	0.3961
	SD	0.0272	0.0168	0.0176	0.0148	0.0160
125 genes (dataset 4)	Avg	0.2275	0.2197	0.2216	0.2177	0.2210
	Best	0.2111	0.2043	0.2053	0.2056	0.2087
	Worst	0.2497	0.2383	0.2468	0.2299	0.2374
	SD	0.0105	0.0096	0.0112	0.0068	0.0087

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