Table 2 Parameter tuning and selection method used in this study

Let S be the set of training samples

Let CR be the crossover rate and MR be the mutation rate

Let k be the number of cross validation folds, where k = 5 is fixed

Let α be the Accuracy to Elimination Ratio

Define the GA parameters apart from CR and MR as those highlighted in Table 1

Define α to belong to the set (0, 0.1, 0.2, …, 0.9, 1]

Define CR to belong to the set (0.5, 0.55, 0.6, …, 0.95, 1]

Define MR to belong to the set [0, 0.05, 0.1, …, 0.45, 0.5)

For each combination of { α, CR, MR}:

- Perform k-fold cross validation using the classifier and gene masking on the set of samples S

- Report the results obtained by the best performing gene mask

- Repeat for 10 iterations

Select the best performing combination of { α, CR, MR} for testing and reporting