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Let S be the set of training samples
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Let CR be the crossover rate and MR be the mutation rate
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Let k be the number of cross validation folds, where k = 5 is fixed
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Let α be the Accuracy to Elimination Ratio
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Define the GA parameters apart from CR and MR as those highlighted in Table 1
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Define α to belong to the set (0, 0.1, 0.2, …, 0.9, 1]
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Define CR to belong to the set (0.5, 0.55, 0.6, …, 0.95, 1]
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Define MR to belong to the set [0, 0.05, 0.1, …, 0.45, 0.5)
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For each combination of { α, CR, MR}:
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- Perform k-fold cross validation using the classifier and gene masking on the set of samples S
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- Report the results obtained by the best performing gene mask
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- Repeat for 10 iterations
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Select the best performing combination of { α, CR, MR} for testing and reporting
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