How can I incorporate a for loop into my genetic algorithm? - matlab

Question

I'm doing a genetic algorithm that attempts to find an optimized solution over the course of 100 generations. My code as is will find 2 generations. I'm trying to find a way to add a for loop in order to repeat the process for the full duration of 100 generations.
clc,clear
format shorte
k=80;
mu=50;
s=.05;
c1=k+(4/3)*mu;
c2=k-(2/3)*mu;
for index=1:50 %6 traits generated at random 50 times
a=.005*rand-.0025;
b=.005*rand-.0025;
c=.005*rand-.0025;
d=.005*rand-.0025;
e=.005*rand-.0025;
f=.005*rand-.0025;
E=[c1,c2,c2,0,0,0;
c2,c1,c2,0,0,0;
c2,c2,c1,0,0,0;
0,0,0,mu,0,0;
0,0,0,0,mu,0;
0,0,0,0,0,mu];
S=[a;d;f;2*b;2*c;2*e];
G=E*S;
g=G(1);
h=G(2);
i=G(3);
j=G(4);
k=G(5);
l=G(6);
F=[(g-h)^2+(h-i)^2+(i-g)^2+6*(j^2+k^2+l^2)];
PI=((F-(2*s^2))/(2*s^2))^2; %cost function, fitness assessed
RP(index,:)=[a,b,c,d,e,f,PI]; %initial random population
end
Gen1=sortrows(RP,7,{'descend'}); %the initial population ranked
%for loop 1:100 would start here
children=zeros(10,6); %10 new children created from the top 20 parents
babysitter=1;
for parent=1:2:20
theta=rand(1);
traita=theta*Gen1(parent,1)+(1-theta)*Gen1(1+parent,1);
theta=rand(1);
traitb=theta*Gen1(parent,2)+(1-theta)*Gen1(1+parent,2);
theta=rand(1);
traitc=theta*Gen1(parent,3)+(1-theta)*Gen1(1+parent,3);
theta=rand(1);
traitd=theta*Gen1(parent,4)+(1-theta)*Gen1(1+parent,4);
theta=rand(1);
traite=theta*Gen1(parent,5)+(1-theta)*Gen1(1+parent,5);
theta=rand(1);
traitf=theta*Gen1(parent,6)+(1-theta)*Gen1(1+parent,6);
children(babysitter,:)=[traita,traitb,traitc,traitd,traite,traitf];
babysitter=babysitter+1;
end
top10parents=Gen1(1:10,1:6);
Gen1([11:50],:)=[]; %bottom 40 parents removed
for newindex=1:30 %6 new traits generated randomly 30 times
newa=.005*rand-.0025;
newb=.005*rand-.0025;
newc=.005*rand-.0025;
newd=.005*rand-.0025;
newe=.005*rand-.0025;
newf=.005*rand-.0025;
newgenes(newindex,:)=[newa,newb,newc,newd,newe,newf];
end
nextgen=[top10parents;children;newgenes]; %top 10 parents, the 10 new children, and the new 30 random traits added into one new matrix
for new50=1:50
newS=[nextgen(new50,1);nextgen(new50,4);nextgen(new50,6);2*nextgen(new50,2);2*nextgen(new50,3);2*nextgen(new50,5)];
newG=E*newS;
newg=newG(1);
newh=newG(2);
newi=newG(3);
newj=newG(4);
newk=newG(5);
newl=newG(6);
newF=[(newg-newh)^2+(newh-newi)^2+(newi-newg)^2+6*(newj^2+newk^2+newl^2)]; %von-Mises criterion
newPI=((newF-(2*s^2))/(2*s^2))^2; %fitness assessed for new generation
PIcolumn(new50,:)=[newPI];
end
nextgenwPI=[nextgen,PIcolumn]; %pi column added to nextgen matrix
Gen2=sortrows(nextgenwPI,7,{'descend'}) %generation 2 ranked
So my question is, how can I get the generations to count themselves in order to make the for loop work. I've searched for an answer and I've read that having matrices count themselves is not a good idea. However, I'm not sure how I could do this besides finding a way to make a genN matrix that counts upward in increments of 1 after the first generation. Any suggestions?
Thank you