Selection

$Selection : P \times F \longmapsto P \times F$

Based on the individual fitness values the relative weight of the individual members with regard to the whole population is computed and written into column 9. Then will in column 10 the expected amount of members be computed, simply rounded up to integers. These are used as the new count for the new numbers of each member.

 --> [POP]=newMember(POP,l,n)
 POP  =
         column 1 to 9
 
    0.    1.    1.    0.    1.    13.    169.    0.1444444    0.5777778  
    1.    1.    0.    0.    0.    24.    576.    0.4923077    1.9692308  
    0.    1.    0.    0.    0.    8.     64.     0.0547009    0.2188034  
    1.    0.    0.    1.    1.    19.    361.    0.3085470    1.234188   
 
         column 10
 
    1.  
    2.  
    0.  
    1.

Based on the new numbers in column 10 a new generation of POP is computed by copying the successful members from 'left' into the area 'right' starting at column 11.

-->p=5, [POP]=newPop(POP,l,p,n)
 POP  =
 
 
         column 1 to 9
 
    0.    1.    1.    0.    1.    13.    169.    0.1444444    0.5777778  
    1.    1.    0.    0.    0.    24.    576.    0.4923077    1.9692308  
    0.    1.    0.    0.    0.    8.     64.     0.0547009    0.2188034  
    1.    0.    0.    1.    1.    19.    361.    0.3085470    1.234188   
 
         column 10 to 15
 
    1.    0.    1.    1.    0.    1.  
    2.    1.    1.    0.    0.    0.  
    0.    1.    1.    0.    0.    0.  
    1.    1.    0.    0.    1.    1.

Then all the strings at the right position will be randomly selected -the 'k1' index- and copied to the position to the left -the j-index-. This is used as a preparation for the mating procedure using crossover.