How to use Sigma Scaling in a genetic algorithm
Well I just tried this :

(1.0/f(x,y))+1.0

a) 21.37858466 | After the reciprocate | 1.046775781
b) 1.425432689 | After the reciprocate | 1.701541369
c) 15.48548031 | After the reciprocate | 1.064576621
d) 14.56668667 | After the reciprocate | 1.068649791
e) 41.0387647  | After the reciprocate | 1.024367205


With these new fitness I used S=1


  fscaled = S + (fraw −fmean)/2σ


Mean = 1.181182154
Dev Std = 0.260649227

And this was my final result : 

a) 0.742170015 | 14.8434003 %
b) 1.998198272 | 39.96396544 %
c) 0.776317134 | 15.52634267 %
d) 0.784130642 | 15.68261284 %
e) 0.699183937 | 13.98367874 %


These can be used with proportional selections.
I think this is the right way to use sigma scaling.
And I used the reciprocate plus one to make this a minimization problem.