function [xv,fv]=GMGA(fitness,a,b,NP,NG,Pc,Pm,alpha,Pbm,eps)
%大变异遗传算法
L = ceil(log2((b-a)/eps+1));
x = zeros(NP,L);
for i=1:NP
x(i,:) = Initial(L);
fx(i) = fitness(Dec(a,b,x(i,:),L));
end
for k=1:NG
sumfx = sum(fx);
favg = sumfx/NP;
[fmax,xmax] = max(fx);
if k<NG/2
if fmax*alpha<favg
rm = rand();
if rm < Pbm
for i=1:(xmax-1)
gmPos = round(rand*(L-1)+1);
x(i,gmPos) = ~x(i,gmPos);
fx(i) = fitness(Dec(a,b,x(i,:),L));
end
for i=(xmax+1):NP
gmPos = round(rand*(L-1)+1);
x(i,gmPos) = ~x(i,gmPos);
fx(i) = fitness(Dec(a,b,x(i,:),L));
end
continue;
end
end
end
Px = fx/sumfx;
PPx = 0;
PPx(1) = Px(1);
for i=2:NP
PPx(i) = PPx(i-1) + Px(i);
end
for i=1:NP
sita = rand();
for n=1:NP
if sita <= PPx(n)
SelFather = n;
break;
end
end
Selmother = round(rand()*(NP-1))+1;
posCut = round(rand()*(L-2)) + 1;
r1 = rand();
if r1<=Pc
nx(i,1:posCut) = x(SelFather,1:posCut);
nx(i,(posCut+1):L) = x(Selmother,(posCut+1):L);
r2 = rand();
if r2 <= Pm
posMut = round(rand()*(L-1) + 1);
nx(i,posMut) = ~nx(i,posMut);
end
else
nx(i,:) = x(SelFather,:);
end
end
x = nx;
for i=1:NP
fx(i) = fitness(Dec(a,b,x(i,:),L));
end
end
fv = -inf;
for i=1:NP
fitx = fitness(Dec(a,b,x(i,:),L));
if fitx > fv
fv = fitx;
xv = Dec(a,b,x(i,:),L);
end
end
function result = Initial(length)
for i=1:length
r = rand();
result(i) = round(r);
end
function y = Dec(a,b,x,L)
base = 2.^((L-1):-1:0);
y = dot(base,x);
y = a + y*(b-a)/(2^L-1);
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