遗传算法及其改进算法的matlab程序

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);