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Table 2 Parameters estimation for GA

From: Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

Prostate dataset

Lung dataset

α

β

Accuracy

#Genes

α

β

Accuracy

#Genes

0.8

0.6

0.9838± 0.0097

2.67± 1.19

0.8

0.6

0.9730± 0.0107

8.65± 2.82

0.8

0.4

0.9899± 0.0072

3.30± 1.02

0.8

0.4

0.9748± 0.0093

7.28± 1.20

0.8

0.25

0.9914± 0.0054

3.52± 0.91

0.8

0.25

0.9801± 0.0106

9.85± 3.12

0.4

0.6

0.9827± 0.0086

2.56± 1.01

0.4

0.6

0.9743± 0.0103

8.80± 3.18

0.4

0.4

0.9912± 0.0069

3.75± 1.44

0.4

0.4

0.9763± 0.0094

9.55± 1.08

0.4

0.25

0.9938 ± 0.0061

4.25 ± 1.95

0.4

0.25

0.9849 ± 0.0089

9.32 ± 1.64

0.1

0.6

0.9837 ± 0.0104

3.04 ± 1.71

0.1

0.6

0.9770 ± 0.0095

7.83 ± 2.06

0.1

0.4

0.9895 ± 0.0065

2.88 ± 0.70

0.1

0.4

0.9763 ± 0.0118

9.63 ± 2.53

0.1

0.25

0.9966 ± 0.0041

4.73 ± 2.10

0.1

0.25

0.9854 ± 0.0101

12.78 ± 1.61

  1. Parameter estimation for the α and β parameters of the fitness function of the GA for the Lung and Prostate datasets.