## adding linear interpolation to a fitness equation

So I’m creating a dynamic state-variable model and I want to add in a forgetting rate for an informational state, however that’ll make the values non-integers and thus I need to do linear interpolation. How do I add that in to my fitness equation?

Sorry for the overwhelming amount of code

%constants

m1=0.9;

m2=0.3;

n1=0.3;

n2=0.9;

b=0.9;

crit=3;

cap=15;

term=20;

%arrays and zeros

opt=zeros(cap,cap, cap,term);

Fd=zeros(cap, cap, cap,term);

Fdd=zeros(cap,cap,cap,term);

Fdb=zeros(cap,cap, cap,term);

Fdbb=zeros(cap,cap,cap,term);

Ft=zeros(cap,cap,cap,term);

x=linspace(1,cap,cap)’;

z=linspace(1,cap,cap)’;

y=linspace(1,cap,cap)’;

f=@(x,y) x-y;

M=f(z.’,y);

p=zeros(size(M));

p2=zeros(size(M));

p3=zeros(size(M));

p4=zeros(size(M));

p5=zeros(size(M));

%z and y

z1=z+1; %dem went to patch 1 and found food

z1(z1>cap)=cap;

z2=z-1; %dem went to patch 1 and didn’t find food

z2(z2<1)=1;

zp=(z1+1); %dem went to patch 1 and found food; obs went to patch 1 and found food

zp(zp>cap)=cap;

zpp=(z1-1); %dem went to patch1 and found food; obs went to patch 1 and didn’t find food

zpp(zpp<1)=1;

zd=(z2+1); %dem went to patch1 and didn’t find food; obs went to patch 1 and found food

zd(zd>cap)=cap;

zdd=(z2-1); %dem went to patch 1 and didn’t find food; obs went to patch 1 and didn’t find food

zdd(zdd<1)=1;

zg=(z+1); %obs went to patch 1 and found food

zg(zg>cap)=cap;

zgg=(z-1); %obs went to patch 1 and didn’t find food

zgg(zgg<1)=1;

y1=y+1; %dem went to patch 2 and found food

y1(y1>cap)=cap;

y2=y-1; %dem went to patch 2 and didn’t find food

y2(y2<1)=1;

yp=(y1+1); %dem went to patch 2 and found food; obs went to patch 2 and found food

yp(yp>cap)=cap;

ypp=(y1-1); %dem went to patch 2 and found food; obs went to patch 2 and didn’t food

ypp(ypp<1)=1;

yd=(y2+1); %dem went to patch 2 and didn’t find food; obs went to patch 2 and found food

yd(yd>cap)=cap;

ydd=(y2-1); %dem went to patch 2 and didn’t find food; obs went to patch 2 and didn’t find food

ydd(ydd<1)=1;

yg=(y+1); %obs went to patch 2 and found food

yg(yg>cap)=cap;

ygg=(y-1); %obs went to patch 2 and didn’t find food

ygg(ygg<1)=1;

%physical states

xp=x-1-1+3; %obs watched dem and found food

xp(xp>cap)=cap;

xpp=x-1-1; %obs watched dem and didn’t find food

xpp(xpp<1)=1;

xd=x-1+3; %obs didn’t watch and found food

xd(xd>cap)=cap;

xdd=x-1; %obs didn’t watch and didn’t find food

xdd(xdd<1)=1;

%specify fitness

Fd(x<=crit,:, :, :)=0;

Fd(x>crit,:,:,:)=1;

Fdb(x<=crit,:,:,:)=0;

Fdb(x>crit,:,:,:)=1;

%specify prob matrix of going to patch 1 for obs

for j=1:15

for k=1:15

if M(j,z1(k))>0

p(j,z1(k))=0.9;

elseif M(j,z1(k))<0

p(j,z1(k))=0.1;

else

p(j,z1(k))=0.5;

end

if M(j,z2(k))>0

p2(j,z2(k))=0.9;

elseif M(j,z2(k))<0

p2(j,z2(k))=0.1;

else

p2(j,z2(k))=0.5;

end

if M(y1(j),k)>0

p3(y1(j),k)=0.9;

elseif M(y1(j),k)<0

p3(y1(j),k)=0.1;

else

p3(y1(j),k)=0.5;

end

if M(y2(j),k)>0

p4(y2(j),k)=0.9;

elseif M(y2(j),k)<0

p4(y2(j),k)=0.1;

else

p4(y2(j),k)=0.5;

end

if M(y(j),z(k))>0

p5(y(j),z(k))=0.9;

elseif M(y(j),z(k))<0

p5(y(j),z(k))=0.1;

else

p5(y(j),z(k))=0.5;

end

end

end

%fitness equations for watching a dem (Fdd) and not watching a dem (Fdbb)

for tt=19:-1:1

for i=1:15

for j=1:15

for k=1:15

Fdd(i,j,k,tt)= b*(…

m1*(…

p(j,z1(k))*(n1*Fd(xp(i),zp(j),y(k),tt+1) + (1-n1)*Fd(xpp(i),zpp(j),y(k),tt+1))+…

(1-p(j,z1(k)))*(n2*Fd(xp(i),z1(j),yg(k),tt+1) + (1-n2)*Fd(xpp(i),z1(j),ygg(k),tt+1)))+…

(1-m1)*(…

p2(j,z2(k))*(n1*Fd(xp(i),zd(j),y(k),tt+1) + (1-n1)*Fd(xpp(i),zdd(j),y(k),tt+1))+…

(1-p2(j,z2(k)))*(n2*Fd(xp(i),z2(j),yg(k),tt+1) + (1-n2)*Fd(xpp(i),z2(j),ygg(k),tt+1))…

))+…

(1-b)*( …

m2*(…

p3(y1(j),k)*(n1*Fd(xp(i),zg(j),y1(k),tt+1) + (1-n1)*Fd(xpp(i) ,zgg(j),y1(k),tt+1))+ …

(1-p3(y1(j),k))*(n2*Fd(xp(i),z(j),yp(k),tt+1) + (1-n2)*Fd(xpp(i) ,z(j),ypp(k),tt+1)))+ …

(1-m2)*(p4(y2(j),k)*(n1*Fd(xp(i),zg(j),y2(k),tt+1) + (1-n1)*Fd(xpp(i),zgg(j),y2(k),tt+1))+…

(1-p4(y2(j),k))*(n2*Fd(xp(i),z(j),yd(k),tt+1) + (1-n2)*Fd(xpp(i),z(j),ydd(k),tt+1))));

Fdbb(i,j,k,tt)= p5(j,k)*( …

n1*Fdb(xd(i),zg(j),y(k),tt+1) + (1-n1)*Fdb(xdd(i),zgg(j),y(k),tt+1) …

)+…

(1-p5(j,k))*( …

n2*Fdb(xd(i),z(j),yg(k),tt+1) + (1-n2)*Fdb(xdd(i),z(j),ygg(k),tt+1)) …

;

%optimal decision and fitness

if Fdd(i,j,k,tt)>Fdbb(i,j,k,tt)

opt(i,j,k,tt)=1;

Ft(i,j,k,tt)=Fdd(i,j,k,tt);

elseif Fdd(i,j,k,tt)<Fdbb(i,j,k,tt)

opt(i,j,k,tt)=2;

Ft(i,j,k,tt)=Fdbb(i,j,k,tt);

elseif Fdd(i,j,k,tt)==Fdbb(i,j,k,tt)

opt(i,j,k,tt)=3;

Ft(i,j,k,tt)=Fdd(i,j,k,tt);

end

end

end

end

end

I would like to add in a forgetting rate so that it looks more like this:

l=0.95;

zp=l*(z1+1); %dem went to patch 1 and found food; obs went to patch 1 and found food

zp(zp>cap)=cap;

zpp=l*(z1-1); %dem went to patch1 and found food; obs went to patch 1 and didn’t find food

zpp(zpp<1)=1;

zd=l*(z2+1); %dem went to patch1 and didn’t find food; obs went to patch 1 and found food

zd(zd>cap)=cap;

zdd=l*(z2-1); %dem went to patch 1 and didn’t find food; obs went to patch 1 and didn’t find food

zdd(zdd<1)=1;

zg=l*(z+1); %obs went to patch 1 and found food

zg(zg>cap)=cap;

zgg=l*(z-1); %obs went to patch 1 and didn’t find food

zgg(zgg<1)=1;

%this would be added to the y’s as well

How do I edit the fitness equation to add in linear interpolation?

note that Fdd is a 4-D matrix, because it has a physical state (x), two infomational states (z and y), and then time (t)

most of the interpolation I see modifies 1-D matricesSo I’m creating a dynamic state-variable model and I want to add in a forgetting rate for an informational state, however that’ll make the values non-integers and thus I need to do linear interpolation. How do I add that in to my fitness equation?

Sorry for the overwhelming amount of code

%constants

m1=0.9;

m2=0.3;

n1=0.3;

n2=0.9;

b=0.9;

crit=3;

cap=15;

term=20;

%arrays and zeros

opt=zeros(cap,cap, cap,term);

Fd=zeros(cap, cap, cap,term);

Fdd=zeros(cap,cap,cap,term);

Fdb=zeros(cap,cap, cap,term);

Fdbb=zeros(cap,cap,cap,term);

Ft=zeros(cap,cap,cap,term);

x=linspace(1,cap,cap)’;

z=linspace(1,cap,cap)’;

y=linspace(1,cap,cap)’;

f=@(x,y) x-y;

M=f(z.’,y);

p=zeros(size(M));

p2=zeros(size(M));

p3=zeros(size(M));

p4=zeros(size(M));

p5=zeros(size(M));

%z and y

z1=z+1; %dem went to patch 1 and found food

z1(z1>cap)=cap;

z2=z-1; %dem went to patch 1 and didn’t find food

z2(z2<1)=1;

zp=(z1+1); %dem went to patch 1 and found food; obs went to patch 1 and found food

zp(zp>cap)=cap;

zpp=(z1-1); %dem went to patch1 and found food; obs went to patch 1 and didn’t find food

zpp(zpp<1)=1;

zd=(z2+1); %dem went to patch1 and didn’t find food; obs went to patch 1 and found food

zd(zd>cap)=cap;

zdd=(z2-1); %dem went to patch 1 and didn’t find food; obs went to patch 1 and didn’t find food

zdd(zdd<1)=1;

zg=(z+1); %obs went to patch 1 and found food

zg(zg>cap)=cap;

zgg=(z-1); %obs went to patch 1 and didn’t find food

zgg(zgg<1)=1;

y1=y+1; %dem went to patch 2 and found food

y1(y1>cap)=cap;

y2=y-1; %dem went to patch 2 and didn’t find food

y2(y2<1)=1;

yp=(y1+1); %dem went to patch 2 and found food; obs went to patch 2 and found food

yp(yp>cap)=cap;

ypp=(y1-1); %dem went to patch 2 and found food; obs went to patch 2 and didn’t food

ypp(ypp<1)=1;

yd=(y2+1); %dem went to patch 2 and didn’t find food; obs went to patch 2 and found food

yd(yd>cap)=cap;

ydd=(y2-1); %dem went to patch 2 and didn’t find food; obs went to patch 2 and didn’t find food

ydd(ydd<1)=1;

yg=(y+1); %obs went to patch 2 and found food

yg(yg>cap)=cap;

ygg=(y-1); %obs went to patch 2 and didn’t find food

ygg(ygg<1)=1;

%physical states

xp=x-1-1+3; %obs watched dem and found food

xp(xp>cap)=cap;

xpp=x-1-1; %obs watched dem and didn’t find food

xpp(xpp<1)=1;

xd=x-1+3; %obs didn’t watch and found food

xd(xd>cap)=cap;

xdd=x-1; %obs didn’t watch and didn’t find food

xdd(xdd<1)=1;

%specify fitness

Fd(x<=crit,:, :, :)=0;

Fd(x>crit,:,:,:)=1;

Fdb(x<=crit,:,:,:)=0;

Fdb(x>crit,:,:,:)=1;

%specify prob matrix of going to patch 1 for obs

for j=1:15

for k=1:15

if M(j,z1(k))>0

p(j,z1(k))=0.9;

elseif M(j,z1(k))<0

p(j,z1(k))=0.1;

else

p(j,z1(k))=0.5;

end

if M(j,z2(k))>0

p2(j,z2(k))=0.9;

elseif M(j,z2(k))<0

p2(j,z2(k))=0.1;

else

p2(j,z2(k))=0.5;

end

if M(y1(j),k)>0

p3(y1(j),k)=0.9;

elseif M(y1(j),k)<0

p3(y1(j),k)=0.1;

else

p3(y1(j),k)=0.5;

end

if M(y2(j),k)>0

p4(y2(j),k)=0.9;

elseif M(y2(j),k)<0

p4(y2(j),k)=0.1;

else

p4(y2(j),k)=0.5;

end

if M(y(j),z(k))>0

p5(y(j),z(k))=0.9;

elseif M(y(j),z(k))<0

p5(y(j),z(k))=0.1;

else

p5(y(j),z(k))=0.5;

end

end

end

%fitness equations for watching a dem (Fdd) and not watching a dem (Fdbb)

for tt=19:-1:1

for i=1:15

for j=1:15

for k=1:15

Fdd(i,j,k,tt)= b*(…

m1*(…

p(j,z1(k))*(n1*Fd(xp(i),zp(j),y(k),tt+1) + (1-n1)*Fd(xpp(i),zpp(j),y(k),tt+1))+…

(1-p(j,z1(k)))*(n2*Fd(xp(i),z1(j),yg(k),tt+1) + (1-n2)*Fd(xpp(i),z1(j),ygg(k),tt+1)))+…

(1-m1)*(…

p2(j,z2(k))*(n1*Fd(xp(i),zd(j),y(k),tt+1) + (1-n1)*Fd(xpp(i),zdd(j),y(k),tt+1))+…

(1-p2(j,z2(k)))*(n2*Fd(xp(i),z2(j),yg(k),tt+1) + (1-n2)*Fd(xpp(i),z2(j),ygg(k),tt+1))…

))+…

(1-b)*( …

m2*(…

p3(y1(j),k)*(n1*Fd(xp(i),zg(j),y1(k),tt+1) + (1-n1)*Fd(xpp(i) ,zgg(j),y1(k),tt+1))+ …

(1-p3(y1(j),k))*(n2*Fd(xp(i),z(j),yp(k),tt+1) + (1-n2)*Fd(xpp(i) ,z(j),ypp(k),tt+1)))+ …

(1-m2)*(p4(y2(j),k)*(n1*Fd(xp(i),zg(j),y2(k),tt+1) + (1-n1)*Fd(xpp(i),zgg(j),y2(k),tt+1))+…

(1-p4(y2(j),k))*(n2*Fd(xp(i),z(j),yd(k),tt+1) + (1-n2)*Fd(xpp(i),z(j),ydd(k),tt+1))));

Fdbb(i,j,k,tt)= p5(j,k)*( …

n1*Fdb(xd(i),zg(j),y(k),tt+1) + (1-n1)*Fdb(xdd(i),zgg(j),y(k),tt+1) …

)+…

(1-p5(j,k))*( …

n2*Fdb(xd(i),z(j),yg(k),tt+1) + (1-n2)*Fdb(xdd(i),z(j),ygg(k),tt+1)) …

;

%optimal decision and fitness

if Fdd(i,j,k,tt)>Fdbb(i,j,k,tt)

opt(i,j,k,tt)=1;

Ft(i,j,k,tt)=Fdd(i,j,k,tt);

elseif Fdd(i,j,k,tt)<Fdbb(i,j,k,tt)

opt(i,j,k,tt)=2;

Ft(i,j,k,tt)=Fdbb(i,j,k,tt);

elseif Fdd(i,j,k,tt)==Fdbb(i,j,k,tt)

opt(i,j,k,tt)=3;

Ft(i,j,k,tt)=Fdd(i,j,k,tt);

end

end

end

end

end

I would like to add in a forgetting rate so that it looks more like this:

l=0.95;

zp=l*(z1+1); %dem went to patch 1 and found food; obs went to patch 1 and found food

zp(zp>cap)=cap;

zpp=l*(z1-1); %dem went to patch1 and found food; obs went to patch 1 and didn’t find food

zpp(zpp<1)=1;

zd=l*(z2+1); %dem went to patch1 and didn’t find food; obs went to patch 1 and found food

zd(zd>cap)=cap;

zdd=l*(z2-1); %dem went to patch 1 and didn’t find food; obs went to patch 1 and didn’t find food

zdd(zdd<1)=1;

zg=l*(z+1); %obs went to patch 1 and found food

zg(zg>cap)=cap;

zgg=l*(z-1); %obs went to patch 1 and didn’t find food

zgg(zgg<1)=1;

%this would be added to the y’s as well

How do I edit the fitness equation to add in linear interpolation?

note that Fdd is a 4-D matrix, because it has a physical state (x), two infomational states (z and y), and then time (t)

most of the interpolation I see modifies 1-D matrices So I’m creating a dynamic state-variable model and I want to add in a forgetting rate for an informational state, however that’ll make the values non-integers and thus I need to do linear interpolation. How do I add that in to my fitness equation?

Sorry for the overwhelming amount of code

%constants

m1=0.9;

m2=0.3;

n1=0.3;

n2=0.9;

b=0.9;

crit=3;

cap=15;

term=20;

%arrays and zeros

opt=zeros(cap,cap, cap,term);

Fd=zeros(cap, cap, cap,term);

Fdd=zeros(cap,cap,cap,term);

Fdb=zeros(cap,cap, cap,term);

Fdbb=zeros(cap,cap,cap,term);

Ft=zeros(cap,cap,cap,term);

x=linspace(1,cap,cap)’;

z=linspace(1,cap,cap)’;

y=linspace(1,cap,cap)’;

f=@(x,y) x-y;

M=f(z.’,y);

p=zeros(size(M));

p2=zeros(size(M));

p3=zeros(size(M));

p4=zeros(size(M));

p5=zeros(size(M));

%z and y

z1=z+1; %dem went to patch 1 and found food

z1(z1>cap)=cap;

z2=z-1; %dem went to patch 1 and didn’t find food

z2(z2<1)=1;

zp=(z1+1); %dem went to patch 1 and found food; obs went to patch 1 and found food

zp(zp>cap)=cap;

zpp=(z1-1); %dem went to patch1 and found food; obs went to patch 1 and didn’t find food

zpp(zpp<1)=1;

zd=(z2+1); %dem went to patch1 and didn’t find food; obs went to patch 1 and found food

zd(zd>cap)=cap;

zdd=(z2-1); %dem went to patch 1 and didn’t find food; obs went to patch 1 and didn’t find food

zdd(zdd<1)=1;

zg=(z+1); %obs went to patch 1 and found food

zg(zg>cap)=cap;

zgg=(z-1); %obs went to patch 1 and didn’t find food

zgg(zgg<1)=1;

y1=y+1; %dem went to patch 2 and found food

y1(y1>cap)=cap;

y2=y-1; %dem went to patch 2 and didn’t find food

y2(y2<1)=1;

yp=(y1+1); %dem went to patch 2 and found food; obs went to patch 2 and found food

yp(yp>cap)=cap;

ypp=(y1-1); %dem went to patch 2 and found food; obs went to patch 2 and didn’t food

ypp(ypp<1)=1;

yd=(y2+1); %dem went to patch 2 and didn’t find food; obs went to patch 2 and found food

yd(yd>cap)=cap;

ydd=(y2-1); %dem went to patch 2 and didn’t find food; obs went to patch 2 and didn’t find food

ydd(ydd<1)=1;

yg=(y+1); %obs went to patch 2 and found food

yg(yg>cap)=cap;

ygg=(y-1); %obs went to patch 2 and didn’t find food

ygg(ygg<1)=1;

%physical states

xp=x-1-1+3; %obs watched dem and found food

xp(xp>cap)=cap;

xpp=x-1-1; %obs watched dem and didn’t find food

xpp(xpp<1)=1;

xd=x-1+3; %obs didn’t watch and found food

xd(xd>cap)=cap;

xdd=x-1; %obs didn’t watch and didn’t find food

xdd(xdd<1)=1;

%specify fitness

Fd(x<=crit,:, :, :)=0;

Fd(x>crit,:,:,:)=1;

Fdb(x<=crit,:,:,:)=0;

Fdb(x>crit,:,:,:)=1;

%specify prob matrix of going to patch 1 for obs

for j=1:15

for k=1:15

if M(j,z1(k))>0

p(j,z1(k))=0.9;

elseif M(j,z1(k))<0

p(j,z1(k))=0.1;

else

p(j,z1(k))=0.5;

end

if M(j,z2(k))>0

p2(j,z2(k))=0.9;

elseif M(j,z2(k))<0

p2(j,z2(k))=0.1;

else

p2(j,z2(k))=0.5;

end

if M(y1(j),k)>0

p3(y1(j),k)=0.9;

elseif M(y1(j),k)<0

p3(y1(j),k)=0.1;

else

p3(y1(j),k)=0.5;

end

if M(y2(j),k)>0

p4(y2(j),k)=0.9;

elseif M(y2(j),k)<0

p4(y2(j),k)=0.1;

else

p4(y2(j),k)=0.5;

end

if M(y(j),z(k))>0

p5(y(j),z(k))=0.9;

elseif M(y(j),z(k))<0

p5(y(j),z(k))=0.1;

else

p5(y(j),z(k))=0.5;

end

end

end

%fitness equations for watching a dem (Fdd) and not watching a dem (Fdbb)

for tt=19:-1:1

for i=1:15

for j=1:15

for k=1:15

Fdd(i,j,k,tt)= b*(…

m1*(…

p(j,z1(k))*(n1*Fd(xp(i),zp(j),y(k),tt+1) + (1-n1)*Fd(xpp(i),zpp(j),y(k),tt+1))+…

(1-p(j,z1(k)))*(n2*Fd(xp(i),z1(j),yg(k),tt+1) + (1-n2)*Fd(xpp(i),z1(j),ygg(k),tt+1)))+…

(1-m1)*(…

p2(j,z2(k))*(n1*Fd(xp(i),zd(j),y(k),tt+1) + (1-n1)*Fd(xpp(i),zdd(j),y(k),tt+1))+…

(1-p2(j,z2(k)))*(n2*Fd(xp(i),z2(j),yg(k),tt+1) + (1-n2)*Fd(xpp(i),z2(j),ygg(k),tt+1))…

))+…

(1-b)*( …

m2*(…

p3(y1(j),k)*(n1*Fd(xp(i),zg(j),y1(k),tt+1) + (1-n1)*Fd(xpp(i) ,zgg(j),y1(k),tt+1))+ …

(1-p3(y1(j),k))*(n2*Fd(xp(i),z(j),yp(k),tt+1) + (1-n2)*Fd(xpp(i) ,z(j),ypp(k),tt+1)))+ …

(1-m2)*(p4(y2(j),k)*(n1*Fd(xp(i),zg(j),y2(k),tt+1) + (1-n1)*Fd(xpp(i),zgg(j),y2(k),tt+1))+…

(1-p4(y2(j),k))*(n2*Fd(xp(i),z(j),yd(k),tt+1) + (1-n2)*Fd(xpp(i),z(j),ydd(k),tt+1))));

Fdbb(i,j,k,tt)= p5(j,k)*( …

n1*Fdb(xd(i),zg(j),y(k),tt+1) + (1-n1)*Fdb(xdd(i),zgg(j),y(k),tt+1) …

)+…

(1-p5(j,k))*( …

n2*Fdb(xd(i),z(j),yg(k),tt+1) + (1-n2)*Fdb(xdd(i),z(j),ygg(k),tt+1)) …

;

%optimal decision and fitness

if Fdd(i,j,k,tt)>Fdbb(i,j,k,tt)

opt(i,j,k,tt)=1;

Ft(i,j,k,tt)=Fdd(i,j,k,tt);

elseif Fdd(i,j,k,tt)<Fdbb(i,j,k,tt)

opt(i,j,k,tt)=2;

Ft(i,j,k,tt)=Fdbb(i,j,k,tt);

elseif Fdd(i,j,k,tt)==Fdbb(i,j,k,tt)

opt(i,j,k,tt)=3;

Ft(i,j,k,tt)=Fdd(i,j,k,tt);

end

end

end

end

end

I would like to add in a forgetting rate so that it looks more like this:

l=0.95;

zp=l*(z1+1); %dem went to patch 1 and found food; obs went to patch 1 and found food

zp(zp>cap)=cap;

zpp=l*(z1-1); %dem went to patch1 and found food; obs went to patch 1 and didn’t find food

zpp(zpp<1)=1;

zd=l*(z2+1); %dem went to patch1 and didn’t find food; obs went to patch 1 and found food

zd(zd>cap)=cap;

zdd=l*(z2-1); %dem went to patch 1 and didn’t find food; obs went to patch 1 and didn’t find food

zdd(zdd<1)=1;

zg=l*(z+1); %obs went to patch 1 and found food

zg(zg>cap)=cap;

zgg=l*(z-1); %obs went to patch 1 and didn’t find food

zgg(zgg<1)=1;

%this would be added to the y’s as well

How do I edit the fitness equation to add in linear interpolation?

note that Fdd is a 4-D matrix, because it has a physical state (x), two infomational states (z and y), and then time (t)

most of the interpolation I see modifies 1-D matrices fitness equation, nonlinear, interpolation MATLAB Answers — New Questions