Algorithm 1. Hybrid GA–MLP for optimizing MLP parameters

1: Set GA parameters (Pc, Pm, n, gmax)

2: Encode solutions (MLP parameters) using real value encoding

3: Randomly generate n solutions

4: Calculate the fitness value of each solution by the trained MLPs

5: for i = 1, until gmax do

6:   for i = 1, until n/2 do

7:     Select two parents

8:     Crossover to create two children with Pc

9:     Mutate children with Pm

10:   end for 11: Replace parents with children

12: end for

13: Return the best solution