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Matlab遗传算法工具箱: Matlab遗传算法工具箱

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    <TD align=middle width=150><FONT color=#ffffff><B>作者</B></FONT></TD>
    <TD><FONT color=#ffffff><B>【共享】遗传算法的MATLAB6.5 源程序 </B></FONT></TD></TR>
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    <TD vAlign=top width=150><B>wqjk</B> <BR><IMG 
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    <TD vAlign=top width="100%"><IMG src="view.files/post.gif" align=absMiddle 
      border=0>于 2004-08-20 15:31 <A 
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      <HR color=#000000 noShade SIZE=1>
      <SPAN class=javascript id=text80816>function [x,endPop,bPop,traceInfo] = 
      ga(bounds,eevalFN,eevalOps,startPop,opts,...<BR>termFN,termOps,selectFN,selectOps,xOverFNs,xOverOps,mutFNs,mutOps)<BR><BR>n=nargin;<BR>if 
      n&lt;2 | n==6 | n==10 | n==12<BR>disp('Insufficient arguements') 
      <BR>end<BR>if n&lt;3 %Default eevalation 
      opts.<BR>eevalOps=[];<BR>end<BR>if n&lt;5<BR>opts = [1e-6 1 
      0];<BR>end<BR>if isempty(opts)<BR>opts = [1e-6 1 0];<BR>end<BR><BR>if 
      any(eevalFN&lt;48) %Not using a .m file<BR>if opts(2)==1 %Float 
      ga<BR>e1str=['x=c1; c1(xZomeLength)=', eevalFN ';']; <BR>e2str=['x=c2; 
      c2(xZomeLength)=', eevalFN ';']; <BR>else %Binary 
      ga<BR>e1str=['x=b2f(endPop(j,<IMG alt=Smile src="view.files/smile.gif" 
      width=15>,bounds,bits); endPop(j,xZomeLength)=',...<BR>&nbsp;&nbsp;eevalFN 
      ';'];<BR>end<BR>else %Are using a .m file<BR>if opts(2)==1 %Float 
      ga<BR>e1str=['[c1 c1(xZomeLength)]=' eevalFN '(c1,[gen eevalOps]);']; 
      <BR>e2str=['[c2 c2(xZomeLength)]=' eevalFN '(c2,[gen eevalOps]);']; 
      <BR>else %Binary ga<BR>e1str=['x=b2f(endPop(j,<IMG alt=Smile 
      src="view.files/smile.gif" width=15>,bounds,bits);[x v]=' eevalFN 
      ...<BR>&nbsp;&nbsp;'(x,[gen eevalOps]); endPop(j,<IMG alt=Smile 
      src="view.files/smile.gif" width=15>=[f2b(x,bounds,bits) v];']; 
      <BR>end<BR>end<BR><BR>if n&lt;6 %Default termination 
      information<BR>termOps=[100];<BR>termFN='maxGenTerm';<BR>end<BR>if n&lt;12 
      %Default muatation information<BR>if opts(2)==1 %Float 
      GA<BR>mutFNs=['boundaryMutation multiNonUnifMutation nonUnifMutation 
      unifMutation'];<BR>mutOps=[4 0 0;6 termOps(1) 3;4 termOps(1) 3;4 0 
      0];<BR>else %Binary 
      GA<BR>mutFNs=['binaryMutation'];<BR>mutOps=[0.05];<BR>end<BR>end<BR>if 
      n&lt;10 %默认的交叉信息<BR>if opts(2)==1 %浮点编码<BR>xOverFNs=['arithXover 
      heuristicXover simpleXover'];<BR>xOverOps=[2 0;2 3;2 0];<BR>else %Binary 
      GA<BR>xOverFNs=['simpleXover'];<BR>xOverOps=[0.6];<BR>end<BR>end<BR>if 
      n&lt;9 %Default select opts only i.e. roullete 
      wheel.<BR>selectOps=[];<BR>end<BR>if n&lt;8 %Default select 
      info<BR>selectFN=['normGeomSelect'];<BR>selectOps=[0.08];<BR>end<BR>if 
      n&lt;6 %默认的算法终止准则<BR>termOps=[100];<BR>termFN='maxGenTerm';<BR>end<BR>if 
      n&lt;4 %初始种群为空<BR>startPop=[];<BR>end<BR>if isempty(startPop) 
      %随机生成初始种群<BR>startPop=initializega(80,bounds,eevalFN,eevalOps,opts(1:2));<BR>end<BR><BR>if 
      opts(2)==0 
      %二进制编码<BR>bits=calcbits(bounds,opts(1));<BR>end<BR><BR>xOverFNs=parse(xOverFNs);<BR>mutFNs=parse(mutFNs);<BR><BR>xZomeLength 
      = size(startPop,2); &nbsp;&nbsp;%Length of the 
      xzome=numVars+fittness<BR>numVar = xZomeLength-1; %变量数目<BR>popSize = 
      size(startPop,1); %种群中个体数目<BR>endPop = zeros(popSize,xZomeLength); 
      %次种群矩阵<BR>c1 = zeros(1,xZomeLength); &nbsp;&nbsp; %个体<BR>c2 = 
      zeros(1,xZomeLength); &nbsp;&nbsp;&nbsp;&nbsp;%个体<BR>numXOvers = 
      size(xOverFNs,1); &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;%交叉操作次数<BR>numMuts = 
      size(mutFNs,1); &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;%变异操作次数<BR>epsilon = 
      opts(1); &nbsp;&nbsp;&nbsp;&nbsp;%适应度门限值<BR>oeval = 
      max(startPop(:,xZomeLength)); %初始种群中的最优值<BR>bFoundIn = 1;<BR>done = 
      0;<BR>gen = 1;<BR>collectTrace = (nargout&gt;3);<BR>floatGA = 
      opts(2)==1;<BR>display = opts(3);<BR><BR>while(~done)<BR>[beval,bindx] = 
      max(startPop(:,xZomeLength)); %当前种群的最优值<BR>best = startPop(bindx,<IMG 
      alt=Smile src="view.files/smile.gif" width=15>;<BR><BR>if 
      collectTrace<BR>traceInfo(gen,1)=gen; &nbsp;&nbsp;&nbsp;&nbsp; 
      %当前代<BR>traceInfo(gen,2)=startPop(bindx,xZomeLength); 
      %最优适应度<BR>traceInfo(gen,3)=mean(startPop(:,xZomeLength)); 
      %平均适应度<BR>traceInfo(gen,4)=std(startPop(:,xZomeLength)); <BR>end<BR><BR>if 
      ( (abs(beval - oeval)&gt;epsilon) | (gen==1)) <BR>if 
      display<BR>fprintf(1,'\n%d %f\n',gen,beval); <BR>end<BR>if 
      floatGA<BR>bPop(bFoundIn,<IMG alt=Smile src="view.files/smile.gif" 
      width=15>=[gen startPop(bindx,<IMG alt=Smile src="view.files/smile.gif" 
      width=15>]; <BR>else<BR>bPop(bFoundIn,<IMG alt=Smile 
      src="view.files/smile.gif" width=15>=[gen 
      b2f(startPop(bindx,1:numVar),bounds,bits)...<BR>&nbsp;&nbsp; 
      startPop(bindx,xZomeLength)];<BR>end<BR>bFoundIn=bFoundIn+1; 
      <BR>oeval=beval; <BR>else<BR>if display<BR>fprintf(1,'%d 
      ',gen);&nbsp;&nbsp; <BR>end<BR>end<BR><BR>endPop = 
      feeval(selectFN,startPop,[gen selectOps]); %选择操作<BR><BR>if floatGA <BR>for 
      i=1:numXOvers,<BR>for j=1:xOverOps(i,1),<BR>&nbsp;&nbsp;a = 
      round(rand*(popSize-1)+1); %一个父代个体<BR>&nbsp;&nbsp;b = 
      round(rand*(popSize-1)+1); 
      %另一个父代个体<BR>&nbsp;&nbsp;xN=deblank(xOverFNs(i,<IMG alt=Smile 
      src="view.files/smile.gif" width=15>); %交叉函数<BR>&nbsp;&nbsp;[c1 c2] = 
      feeval(xN,endPop(a,<IMG alt=Smile src="view.files/smile.gif" 
      width=15>,endPop(b,<IMG alt=Smile src="view.files/smile.gif" 
      width=15>,bounds,[gen… xOverOps(i,<IMG alt=Smile 
      src="view.files/smile.gif" width=15>]);<BR>&nbsp;&nbsp;<BR>&nbsp;&nbsp;if 
      c1(1:numVar)==endPop(a,(1:numVar)) <BR>&nbsp;&nbsp; 
      c1(xZomeLength)=endPop(a,xZomeLength); <BR>&nbsp;&nbsp;elseif 
      c1(1:numVar)==endPop(b,(1:numVar))<BR>&nbsp;&nbsp; 
      c1(xZomeLength)=endPop(b,xZomeLength);<BR>&nbsp;&nbsp;else 
      <BR>&nbsp;&nbsp; eeval(e1str);<BR>&nbsp;&nbsp;end<BR>&nbsp;&nbsp;if 
      c2(1:numVar)==endPop(a,(1:numVar))<BR>&nbsp;&nbsp; 
      c2(xZomeLength)=endPop(a,xZomeLength);<BR>&nbsp;&nbsp;elseif 
      c2(1:numVar)==endPop(b,(1:numVar))<BR>&nbsp;&nbsp; 
      c2(xZomeLength)=endPop(b,xZomeLength);<BR>&nbsp;&nbsp;else 
      <BR>&nbsp;&nbsp; eeval(e2str);<BR>&nbsp;&nbsp;end 
      <BR>&nbsp;&nbsp;<BR>&nbsp;&nbsp;endPop(a,<IMG alt=Smile 
      src="view.files/smile.gif" width=15>=c1;<BR>&nbsp;&nbsp;endPop(b,<IMG 
      alt=Smile src="view.files/smile.gif" 
      width=15>=c2;<BR>end<BR>end<BR><BR>for i=1:numMuts,<BR>for 
      j=1:mutOps(i,1),<BR>&nbsp;&nbsp;a = 
      round(rand*(popSize-1)+1);<BR>&nbsp;&nbsp;c1 = 
      feeval(deblank(mutFNs(i,<IMG alt=Smile src="view.files/smile.gif" 
      width=15>),endPop(a,<IMG alt=Smile src="view.files/smile.gif" 
      width=15>,bounds,[gen mutOps(i,<IMG alt=Smile src="view.files/smile.gif" 
      width=15>]);<BR>&nbsp;&nbsp;if c1(1:numVar)==endPop(a,(1:numVar)) 
      <BR>&nbsp;&nbsp; 
      c1(xZomeLength)=endPop(a,xZomeLength);<BR>&nbsp;&nbsp;else<BR>&nbsp;&nbsp; 
      eeval(e1str);<BR>&nbsp;&nbsp;end<BR>&nbsp;&nbsp;endPop(a,<IMG alt=Smile 
      src="view.files/smile.gif" width=15>=c1;<BR>end<BR>end<BR><BR>else 
      %遗传操作的统计模型<BR>for i=1:numXOvers,<BR>xN=deblank(xOverFNs(i,<IMG alt=Smile 
      src="view.files/smile.gif" width=15>); 
      &nbsp;&nbsp;<BR>cp=find(rand(popSize,1)&lt;xOverOps(i,1)==1);<BR>if 
      rem(size(cp,1),2) cp=cp(1<IMG alt=Sad src="view.files/smile_sad.gif" 
      width=15>size(cp,1)-1)); end<BR>cp=reshape(cp,size(cp,1)/2,2);<BR>for 
      j=1:size(cp,1)<BR>&nbsp;&nbsp; a=cp(j,1); b=cp(j,2); <BR>&nbsp;&nbsp;