Algorithm 1. Steps of EFOAOA

1. Input: the dataset which has D features, number of individuals (N), number of iterations ($tmax$), and parameters of EFOAOA First Stage 2. Split data into twp parts (i.e., training and testing) 3. Construct the population $X$ using Equation (18). Second Stage 4. $t=1$ 5. While ($t<tmax$) 6. Convert each $X_i$ into its binary version using Equation (19). 7. Compure fitness value for each $X_i$ based on training set as in Equation (20). 8. Find the best individual $X_b$. 9. Update $X$ using Equation (21). 10. $t=t+1$ 11. EndWhie Third Stage 12. Reduce the testing set based on selected features from $X_b$. 13. Evalaute the performance using different measures