genetic.m

遗传算法用于函数优化问题

function [X,Y,x,y,fx,vx,vmfit,P] = genetic(numfunction,v,ger,pc,pm)
[N,L] = size(v); ger = 200; pc = 0.5; pm = 0.01; updatef=0;c=0;
if nargin == 1
   [N,L] = size(v); ger = 200; pc = 0.5; pm = 0.01;c=0;
end
disp(sprintf('Number of generations: %d',ger));
disp(sprintf('Population size: %d',N));
disp(sprintf('Crossover probability: %.3f',pc));
disp(sprintf('Mutation probability: %.3f',pm));
switch numfunction
 case 1
     f = '-1 * x .* sin(2 * pi .* x) + y.* sin(2 * pi .* y) + 1';
 case 2
     f = '1 * x .* sin(4 * pi .* x) - 1 * y.* sin(4 * pi .* y + pi) + 1';
 case 3
     f = '1+1 * x .* sin(4 * pi .* x) - 1 * y.* sin(4 * pi .* y + pi) + sin(6*sqrt(x.*x+y.*y))./(6*sqrt(x.*x+y.*y+0.000000000000001))'; 
 case 4
     f='-(x.^2+2*y.^2)+0.3*cos(3*3.1415926*x)-0.3*cos(4*3.1415926*y)-0.3';
 case 5
     f='10+sin(1./x)./((x-0.6).*(x-0.6)+0.1)';
 case 6
     f='-(cos(2.*x+1)+2.*cos(3.*x+2)+3.*cos(4.*x+3)+4.*cos(5.*x+4)+5.*cos(6.*x+5)).*(cos(2.*y+1)+2.*cos(3.*y+2)+3.*cos(4.*y+3)+4.*cos(5.*y+4)+5.*cos(6.*y+5))';
     c=1;
 end
[x,y] = meshgrid(-1:0.05:1,-1:0.05:1); vxp = x; vyp = y;
vzp = eval(f); PRINT = 1;
% General parameters & Initial operations
sol1=1; vmfit = []; it = 1; vx = []; C = [];updatef=-10;
x = decod(v(:,1:11),11); y = decod(v(:,12:end),11); fit = eval(f);
plotnow(PRINT,vxp,vyp,vzp,x,y,fit,1,1); title('Initial Population');
disp('Press any key to continue...'); pause;
% Generations
t0 = clock;
while it <= ger %& sol1 <= 2.26,
% Selection
for i=1:N
   sp(i)=(fit(i)+1)/sum(fit(i)+1);
end
for i=2:N
   sp(i)=sp(i-1)+sp(i);
end
for i=1:N
   p=rand(1); sindex=1;
   while p > sp(sindex)
      sindex=sindex+1;
   end
   newv(i,:)=v(sindex,:);
end
for i=1:N
   v(i,:)=newv(i,:);
end
% Crossver
for i=1:N
   cindex(i)=i;
end
for i=1:N
   point=unidrnd(N-i+1);
   temp=cindex(i);
   cindex(i)=cindex(i+point-1);
   cindex(i+point-1)=temp;
end
for i=1:2:N
   p=rand(1);
   if(p<pc)
      point=unidrnd(L-1)+1;
      for j=point:(L-1)
         ch=v(cindex(i),j);
         v(cindex(i),j)=v(cindex(i+1),j);
         v(cindex(i+1),j)=ch;
      end
   end
end
% Mutation
M=rand(N,L)<=pm;
v=v-2.*(v.*M)+M;
% Evaluatefitness & Evolution
x = decod(v(:,1:11),11); y = decod(v(:,12:end),11); fit = eval(f);
[sol1,indb1] = max(fit); 
if updatef>=sol1
   sol1=updatef;
   v(indb1,:)=updatec;
end
updatef=sol1;
updatec=v(indb1,:);
[sol2,indb2] = min(fit);
v(indb2,:) = v(indb1,:);
x = decod(v(:,1:11),11); y = decod(v(:,12:end),11); fit = eval(f);
plotnow(PRINT,vxp,vyp,vzp,x,y,fit,it,5);
media = mean(fit);
vx = [vx sol1]; vmfit = [vmfit media];
    if rem(it,1) == 0 | it == 10,
       if c~=1
          disp(sprintf('Gen.: %d  x: %2.5f  y: %2.5f  Av: %2.2f  f(x,y): %2.5f',it,x(indb1),y(indb1),media,sol1));
      else
          disp(sprintf('Gen.: %d   Av: %2.2f  f(x,y): %2.5f',it,media,sol1));
      end
	end;
	it = it + 1;
end;
T = etime(clock,t0); %F = flops - f0;
X=x;Y=y;
x = x(indb1); y = y(indb1); fx = sol1; P = v;
disp(sprintf('the total time is %2.4f',T));