patrecog.m

以Matlab为平台设计一个用于寻求多峰值函数峰值点的软件对一元及二元多峰值函数的优化；

function [] = patrecog
    load num8_12x10; 
    X = v(1,:);  
    P = cadeia(10,120,0,0,0);
    M = pattern(P,X);
  
% Function CADEIA
function [ab,ag] = cadeia(n1,s1,n2,s2,bip)
if nargin == 2,
   n2 = n1; s2 = s1; bip = 1;
end;
% Antibody (Ab) chains
ab = 2 .* rand(n1,s1) - 1;
if bip == 1,
   ab = hardlims(ab);
else,
   ab = hardlim(ab);
end;
% Antigen (Ag) chains
ag = 2 .* rand(n2,s2) - 1;
if bip == 1,
   ag = hardlims(ag);
else,
   ag = hardlim(ag);
end;
% End Function CADEIA

function [M] = pattern(P,X);
   gen = 100; n = round(size(P,1)/2); pm = 0.05; per = 0.0; beta = 10;
  [N,L] = size(P); it = 0;np = size(X,1); PRINT = 1;
% Hypermutation controlling parameters
pma = pm; itpm = 10; pmr = 0.8;
mfit = []; vpm = []; menor = 1;
M = cadeia(np,L+1);
disp(sprintf('Population size: %d',N));
disp(sprintf('Memory matrix size: [%d,%d]',np,L));
disp(sprintf('Maximum number of generations: %d',gen));
imprime(PRINT,12,10,X,it,1,1); 
title('Pattern to be recognized');
imprime(PRINT,12,10,M(1,2:end),it,1,2);
title('Initial memory matrix');
M=(M+1)/2;M(1,1)=L; % Transform into a binary matrix

% Generations
while it < gen & menor > 0,
   T = []; k = 0; vet = randperm(np); vfit = [];	 vind = [];		% Assincronous
   while k < np,
      k = k+1; fit = []; % i = vet(k); 
      [fit,mXOR] = match(P,X(k,:)); 
      [v(k,:),ind(k,:)] = sort(fit);
      
      % Reproduction
      [T,pcs] = reprod(n,beta,N,ind(k,:),P,T);
      
      % Hypermutation
      Ta = rand(size(T,1),L) <= pm;
      T = T - 2 .* (T.*Ta) + Ta;    % 0,1 mutation
      T(pcs,:) = P(ind(k,1:n),:);	% keep the previous best individuals
      
      % Re-selection
      [fit,mXOR] = match(T,X(k,:));
      pcs = [0 pcs];
      for i=1:n,
         [out(i),bcs(i)] = min(fit(pcs(i)+1:pcs(i+1)));		% Minimization problem
         bcs(i) = bcs(i) + pcs(i);
      end;
      P(ind(k,1:n),:) = T(bcs,:);
      
      % Memory Assignment & Evaluation
      [b,indb] = min(fit);
      if b < M(k,1),
         M(k,1) = b; M(k,2:end) = T(indb,:);
         menor(k) = b;
      else,
         menor(k) = M(k,1);
      end;
      
      % Editing (Repertoire shift)
      nedit = round(per*N);
      if nedit > 0,
         P(ind(k,N-nedit-np+1:N-np),:) = cadeia(nedit,L,0,0,0);
      end;
   end;
   P(ind(:,1),:) = M(:,2:end);
   
   % Hypermutation control
   [pm] = pmcont(pm,pma,pmr,it,itpm);
   
   menor = sum(menor); vfit = [vfit,menor]; it = it + 1;
   disp(sprintf('It.: %d	pm: %.3f	F: %2.4f',it,pm,menor));
   Mem = hardlims(M - 0.1);
   imprime(PRINT,12,10,Mem(:,2:end),it,5,2);
end; % end while
imprime(PRINT,12,10,M(:,2:end),it,1,2);

% Function plot pictures
function [] = imprime(PRINT,res_lin,res_col,P,it,mit,fn);
if PRINT == 1,
   if rem(it,mit) == 0,
      fig(res_lin,res_col,0,fn,P);
   end;
end;

function [mat] = fig(L,C,div,fn,X)
figure(fn); clf; hold on;
for i = 1:size(X,1),
   mat = reshape(X(i,:),C,L)';
   if (div == 1),
      subplot(2,4,i);
   end;
   image(mat*15); axis('square'); axis('off');
end;
drawnow; hold off;

% Function match bipolar strings
function [ms,mXOR] = match(ab,ag)
msc = [];   % ms complement
if min(min(ag)) == -1,
   ag = hardlim(ag);
end;
% Using the XOR operator for calculating the match score
[n1,s1] = size(ab);
ag = ones(n1,1) * ag;					% Multiply the Antigen
mXOR = xor(ab,ag);
ms = sum(mXOR');

% Reproduction
function [T,pcs] = reprod(n,beta,N,ind,P,T);
% n		-> number of clones
% beta	-> multiplying factor
% ind	-> best individuals
% T		-> temporary population
% pcs	-> final position of each clone

   for i=1:n,
      cs(i) = round(beta*N/i);
      pcs(i) = sum(cs);
      T = [T; ones(cs(i),1) * P(ind(i),:)];
   end;


% Control of pm
function [pm] = pmcont(pm,pma,pmr,it,itpm);
% pma	-> initial value
% pmr	-> control rate
% itpm	-> iterations for restoring
if rem(it,itpm) == 0,
   pm = pm * pmr;
   if rem(it,10*itpm) == 0,
      pm = pma;
   end;
end;