Table 7.

Parameters used in the experiments.

GP Parameters
population size	500 individuals
population initialization	ramped half and half [26]
selection method	tournament (tournament size = 10)
crossover rate	0.9
mutation rate	0.1
maximum number of generations	5
algorithm	generational tree based GP with no elitism
SVM Parameters
complexity parameter	0.1
size of the kernel cache	107
epsilon value for the round-off error	10-12
exponent for the polynomial kernel	1.0,2.0, 3.0
tolerance parameter	0.001
Multilayered Perceptron Parameters
learning algorithm	Back-propagation
learning rate	0:03
activation function for all the neurons in the net	sigmoid
momentum	0.2 progressively decreasing until 0.0001
hidden layers	(number of attributes + number of classes)/2
number of epochs of training	500
Random Forest Parameters
number of trees	2500
number of attributes per node	1