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

Algorithm 1

Main pseudocode

1: t:=0;

2: Front (t):= ;

3: Sub-Front (t):= ;

4: Create initial population P (t) at random

5: Calculate () P (t)

6: Order population P (t) according to the niche count               Definition14

7: Inclusion of the individuals of P (t) in Front (t)               using Algorithm2

8: Inclusion of the individuals of P (t) Front (t) in Sub-Front (t)         using Algorithm3

9: for t 1:Number of iterations

10:  Create population G (t)                       using Algorithm4

11:  Calculate () G (t)

12:  Inclusion of the individuals of G (t) in Front (t)               using Algorithm2

13:  Inclusion of the individuals of G (t) Front (t) in Sub-Front (t)         using Algorithm3

14:  Update P (t) with the individuals of G (t)               using Algorithm5

15:  Order population P (t)

16: end for