function [xv,fv]=MMAdapGA(fitness,a,b,NP,NG,Pc1,Pc2,Pm1,Pm2,eps)
%多变异位自适应遗传算法
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);
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;
favg = sumfx/NP;
fmax = max(fx);
fmin = min(fx);
Fitness_f = fx(SelFather);
Fitness_m = fx(Selmother);
Fm = max(Fitness_f,Fitness_m);
if Fm>=favg
Pc = Pc1*(fmax - Fm)/(fmax - favg);
else
Pc = Pc2;
end
r1 = rand();
if r1<=Pc
nx(i,1:posCut) = x(SelFather,1:posCut);
nx(i,(posCut+1):L) = x(Selmother,(posCut+1):L);
fmu = fitness(Dec(a,b,nx(i,:),L));
if fmu>=favg
Pm = Pm1*(fmax - fmu)/(fmax - favg);
else
Pm = Pm2;
end
r2 = rand();
if r2 <= Pm
if fmax ~= fmin
numMut = round( L*(fmax-fmu)/(fmax-fmin)/3);
else
numMut = 1;
end
intArr = 1:L;
posMut = zeros(1,numMut);
for j=1:numMut
tmp = round(rand()*(L- j) + 1);
posMut(j) = intArr(tmp);
intArr = [intArr(1:(tmp-1)) intArr((tmp+1):(L-j+1))];
end
for j=1:numMut
nx(i,posMut(j)) = ~nx(i,posMut(j));
end
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);
