continuousga.m

修改自wiley的书中的各种遗传算法（GA）matlab代码

%PROGRAM 3: CONTINUOUS GENETIC ALGORITHM
% Continuous Genetic Algorithm
%
% minimizes the objective function designated in ff
% Before beginning, set all the parameters in parts
% I, II, and III
% Haupt & Haupt
% 2003
%_______________________________________________________
% I Setup the GA
ff='testfunction'; % objective function
npar=2; % number of optimization variables
varhi=10; varlo=0; % variable limits



%_______________________________________________________
% II Stopping criteria
maxit=100; % max number of iterations
mincost=-9999999; % minimum cost
%_______________________________________________________
% III GA parameters
popsize=12; % set population size
mutrate=.2; % set mutation rate
selection=0.5; % fraction of population kept
Nt=npar; % continuous parameter GA Nt=#variables
keep=floor(selection*popsize); % #population
% members that survive
nmut=ceil((popsize-1)*Nt*mutrate); % total number of
% mutations
M=ceil((popsize-keep)/2); % number of matings
%_______________________________________________________
% Create the initial population
iga=0; % generation counter initialized
par=(varhi-varlo)*rand(popsize,npar)+varlo; % random
cost=feval(ff,par); % calculates population cost using ff
[cost,ind]=sort(cost); % min cost in element 1
par=par(ind,:); % sort continuous
minc(1)=min(cost); % minc contains min of population
meanc(1)=mean(cost); % meanc contains mean of population
%_______________________________________________________
% Iterate through generations
while iga<maxit
iga=iga+1; % increments generation counter
%_______________________________________________________
% Pair and mate
M=ceil((popsize-keep)/2); % number of matings
prob=flipud([1:keep]'/sum([1:keep])); % weights of chromosomes
odds=[0 cumsum(prob(1:keep))']; % probability distribution function
pick1=rand(1,M); % mate #1
pick2=rand(1,M); % mate #2

% ma and pa contain the indicies of the chromosomes
% that will mate
ic=1;
while ic<=M
for id=2:keep+1
if pick1(ic)<=odds(id) & pick1(ic)>odds(id-1)
ma(ic)=id-1;
end
if pick2(ic)<=odds(id) & pick2(ic)>odds(id-1)
pa(ic)=id-1;
end
end
ic=ic+1;
end
%_______________________________________________________
% Performs mating using single point crossover
ix=1:2:keep; % index of mate #1
xp=ceil(rand(1,M)*Nt); % crossover point
r=rand(1,M); % mixing parameter
for ic=1:M
xy=par(ma(ic),xp(ic))-par(pa(ic),xp(ic)); % ma and pa  mate
par(keep+ix(ic),:)=par(ma(ic),:); % 1st offspring
par(keep+ix(ic)+1,:)=par(pa(ic),:); % 2nd offspring
par(keep+ix(ic),xp(ic))=par(ma(ic),xp(ic))-r(ic).*xy;
% 1st
par(keep+ix(ic)+1,xp(ic))=par(pa(ic),xp(ic))+r(ic).*xy;
% 2nd
if xp(ic)<npar % crossover when last variable not selected
par(keep+ix(ic),:)=[par(keep+ix(ic),1:xp(ic))
par(keep+ix(ic)+1,xp(ic)+1:npar)];
par(keep+ix(ic)+1,:)=[par(keep+ix(ic)+1,1:xp(ic))
par(keep+ix(ic),xp(ic)+1:npar)];
end % if
end
%_______________________________________________________
% Mutate the population
mrow=sort(ceil(rand(1,nmut)*(popsize-1))+1);
mcol=ceil(rand(1,nmut)*Nt);



for ii=1:nmut
par(mrow(ii),mcol(ii))=(varhi-varlo)*rand+varlo;
% mutation
end % ii
%_______________________________________________________
% The new offspring and mutated chromosomes are
% evaluated
cost=feval(ff,par);
%_______________________________________________________
% Sort the costs and associated parameters
[cost,ind]=sort(cost);
par=par(ind,:);
%_______________________________________________________
% Do statistics for a single nonaveraging run
minc(iga+1)=min(cost);
meanc(iga+1)=mean(cost);
%_______________________________________________________
% Stopping criteria
if iga>maxit | cost(1)<mincost
break
end
[iga cost(1)]
end %iga
%_______________________________________________________
% Displays the output
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(['popsize = ' num2str(popsize) ' mutrate = ' num2str(mutrate) ' # par = ' num2str(npar)])
disp(['#generations=' num2str(iga) ' best cost=' num2str(cost(1))])
disp(['best solution'])
disp([num2str(par(1,:))])
disp('continuous genetic algorithm')



figure(24)
iters=0:length(minc)-1;
plot(iters,minc,iters,meanc,'-');
xlabel('generation');ylabel('cost');
text(0,minc(1),'best');text(1,minc(2),'population average')