遗传算法 matlab.txt

MATLAB环境下的遗传算法源程序代码压缩包

% bga
%
% This is a typical GA that works with binary
variables
% Uses - uniform crossover
% - tournament selection
%
% Randy Haupt
% 11/21/05
clear
global funcount
funcount=0;
% Defining cost function
nvar=6;
nbits=3;
Nt=nvar*nbits;
ff=’testfun3’;
% GA parameters
npop=8; % population size
mutrate=0.2; % mutation rate
el=1; % number of chromosomes not mutated
Nmut=ceil(mutrate*((npop-el)*Nt)); %# mutations
% stopping criteria
maxgen=400; % max # generations
maxfun=2000; % mas # function calls
mincost=-50; % acceptable cost
% initial population
P=round(rand(npop,Nt));
% cost function
cost=feval(ff,P);
[c,in]=min(cost);
tp=P(1,:); tc=cost(1);
P(1,:)=P(in,:); cost(1)=cost(in);
P(in,:)=tp; cost(in)=tc;
minc(1)=min(cost); % best cost in each generation
for gen=1:maxgen
% Natural selection
indx=find(cost<=mean(cost));
keep=length(indx);
cost=cost(indx); P=P(indx,:);
M=npop-keep;
% Create mating pool using tournament selection
Ntourn=2;
for ic=1:M
rc=ceil(keep*rand(1,Ntourn));
[c,ci]=min(cost(rc)); % indicies of mother
ma=rc(ci);
rc=ceil(keep*rand(1,Ntourn));
[c,ci]=min(cost(rc)); % indicies of father
pa=rc(ci);
% generate mask
mask=round(rand(1,Nt));
% crossover
P(keep+ic,:)=mask.*P(ma,:)+not(mask).*P(pa,:);
end
% Mutation
elP=P(el+1:npop,:);
elP(ceil((npop-el)*Nt*rand(1,Nmut)))=round(rand(1,
Nmut));
P(el+1:npop,:)=elP;
% cost function
cost=feval(ff,P);
[c,in]=min(cost);
tp=P(1,:); tc=cost(1);
P(1,:)=P(in,:); cost(1)=cost(in);
P(in,:)=tp; cost(in)=tc;
minc(gen+1)=cost(1);
[gen cost(1)]
% Convergence check
if funcount>maxfun | gen>maxgen | minc(gen+1)
<mincost
break
end
end
% Present results
day=clock;
disp(datestr(datenum(day(1),day(2),day(3),day(4),day(5),day(6)),0))
disp([‘optimized function is ‘ ff])
format short g
disp([‘# variables = ‘ num2str(nvar) ‘ # bits = ‘
num2str(nbits)])
disp([‘min cost = ‘ num2str(mincost)])
disp([‘best chromosome = ‘ num2str(P(1,:))])
figure(1)
plot([0:gen],minc)
xlabel(‘generation’);ylabel(‘cost’)
% cga
%
% This is a typical GA that works with continuous
variables
% Uses - single point crossover
% - roulette wheel selection
%
% Randy Haupt
% 11/21/05
clear
global funcount
funcount=0;
% Defining cost function
nvar=10;
ff=’testfun3’;
% GA parameters
npop=8; % population size
mutrate=0.15; % mutation rate
natsel=npop/2; % #chromosomes kept
M=npop-natsel; % #chromosomes discarded
el=1; % number of chromosomes not mutated
Nmut=ceil(mutrate*((npop-el)*nvar)); %# mutations
parents=1:natsel; % indicies of parents
prob=parents/sum(parents); % prob assigned to parents
odds=[0 cumsum(prob)]; Nodds=length(odds); % cum prob
% stopping criteria
maxgen=500; % max # generations
maxfun=2000; % mas # function calls
mincost=-50; % acceptable cost
% initial population
P=rand(npop,nvar);
% cost function
cost=feval(ff,P);
% Natural selection
[cost ind]=sort(cost);
P=P(ind(1:natsel),:);
cost=cost(1:natsel);
minc(1)=min(cost); % best cost in each generation
for gen=1:maxgen
% Create mating pool
for ic=1:2:M
r=rand;ma=max(find(odds<r)); % indicies of mother
r=rand;pa=max(find(odds<r)); % indicies of father
xp=ceil(rand*nvar); % crossover point
r=rand; % mixing parameter
xy=P(ma,xp)-P(pa,xp); % mix from ma and pa
% generate masks
mask1=[ones(1,xp) zeros(1,nvar-xp)];
mask2=not(mask1);
% crossover
P(natsel+ic,:)=mask1.*P(ma,:)+mask2.*P(pa,:);
P(natsel+ic+1,:)=mask2.*P(ma,:)+mask1.*P(pa,:);
% create single point crossover variable
P(natsel+ic,xp)=P(natsel+ic,xp)-r*xy;
P(natsel+ic+1,xp)=P(natsel+ic+1,xp)+r*xy;
end
% Mutation
elP=P(el+1:npop,:);
elP(ceil((npop-el)*nvar*rand(1,Nmut)))=rand(1,Nmut);
P(el+1:npop,:)=elP;
% cost function
cost=feval(ff,P);
% Natural selection
[cost ind]=sort(cost);
P=P(ind(1:natsel),:);
cost=cost(1:natsel);
minc(gen+1)=cost(1);
[gen cost(1)]
% Convergence check
if funcount>maxfun | gen>maxgen | minc(gen+1)<
mincost
break
end
end
% Present results
day=clock;
disp(datestr(datenum(day(1),day(2),day(3),day(4),day(5),day(6)),0))
disp([‘optimized function is ‘ ff])
format short g
disp([‘ # par = ‘ num2str(nvar)])
disp([‘min cost = ‘ num2str(mincost)])
disp([‘best chromosome = ‘ num2str(P(1,:))])
figure(1)
plot([0:gen],minc)
xlabel(‘generation’);ylabel(‘cost’)
% a test function with one local minima at xn=0
%
% Randy Haupt
% 11/21/05
function sll=testfun1(chrom)
global funcount
[nr,nc]=size(chrom);
funcount=funcount+nr; % keeps counting number of
function calls
bb=10*(chrom-.5); % transforms chromosome variables
sll(:,1)= bb.^2*ones(nc,1);
% a test function with many local minima at xn=0
%
% Randy Haupt
% 11/21/05
function sll=testfun3(chrom)
global funcount
[nr,nc]=size(chrom);
funcount=funcount+nr; % keeps counting number of
function calls
k=2*pi; % wavenumber
d=0.5; % element spacing
N=2*nc; % number of elements in array
x=(0:(N-1))*d; % element spacing
u=0:2/10/N:1; % u=cos(phi)
Q=exp(j*k*x’*u); % phase
for ic=1:nr
w=[fliplr(chrom(ic,:)) chrom(ic,:)]; % amplitude
weights
af=20*log10(abs(w*Q)).’; % array factor in dB
af=af-max(af); % normalize array factor
saf=flipud(sort(af));
ind=min(find(saf>af));
sll(ic,1)=saf(ind(1)); % max sidelobe level
end