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 |