A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA

Algorithm 4

Create population

1: Choose Front (t) Random selection

2: Choose P (t) According to an exponential distribution ()

3: u random(1)

4: if u> then is the probability of crossover and mutation

5:   and are obtained by crossing over and

6: else

7:   and are obtained by mutating and

8: end if

9: Choose Sub-Front (t) Random selection

10: Choose P (t) According to an exponential distribution ()

11: u random(1)

12: if u> then

13:   and are obtained by crossing over and

14: else

15:   and are obtained by mutating and

16: end if