tutorial.asv

这是一个人工免疫算法的matlab工具箱。该工具箱可以根据不同的优化函数形式设置来用人工免疫算法来寻优

function [] = tutorial
%
% Function TUTORIAL Demonstration
% Runs a Demo for the following immune tools:
% 1) ABNET (Growing Boolean Competitive Network - journal.doc)
% 2) CLONALG (Clonal Selection Algorithm - gecco00.doc)
% 3) AINET (Artificial Immune Network - ainet.doc)
%
% Other Sources of Reference: RT DCA 01/99 (Artificial Immune Systems Part I - Basic Theory and Applications)
%
% Secondary Functions: 
% Internal Subfunctions: PRESENT, CHOOSE, DRAW_ABNET, PLOTVET, CADEIA
% Auxiliary Subfunctions: ABNET, GA3D, IMALG3D, AINET, ANALYSIS, DENDRO
%
% Copyrigth by Leandro Nunes de Castro
% April/May, 2000
%

% Function chooses the demo
while 1,
   p = present; choose(p);
   disp('Press any key to continue...'); pause;
end;


% ------------------------- %
%   INTERNAL SUBFUNCTIONS
% ------------------------- %

% Function presents the DEMO TOOLBOX
function p = present,
clc; disp('*-----------------------------------------------*');
disp(sprintf('ARTIFICIAL IMMUNE SYSTEMS - TUTORIAL PRESENTATION'));
disp(sprintf('	    Leandro Nunes de Castro'));
disp(sprintf('		April/May, 2000'));
disp('*-----------------------------------------------*');
disp(sprintf('Available Demos:'));
disp(sprintf('1) SAND'));
disp(sprintf('2) ABNET'));
disp(sprintf('3) CLONALG'));
disp(sprintf('4) AINET'));
p = input('Type the desired demo number (or CTRL^C to Interrupt): ');
% End function PRESENT


% Function allows choosing Demos
function choose(p);
switch p,
case 1,
   disp(sprintf('\n** PART I - SAND (Simulated ANnealing Approach to Increase Diversity) **'));
   disp(sprintf('Available Demo Tasks:'));
   disp(sprintf('1) REAL-VALUED VECTORS'));
   disp(sprintf('2) 3-D BINARY POINTS'));
   task = input('Type the desired demo number (1) or (2): ');
   switch task,
   case 1,
      N = input('Choose the number of 2-D vectors: ');
      ab = randn(N,2);
      [ab,vE,vRb,E,Rb] = sareal(ab);
      figure(3); plot(vRb); title('Energy Evolution (E)');
      axis([0 length(vRb) 0 105]);
   case 2,
      N = input('Choose the population size (even size) [2,8]: ');
      [ab,vE,E] = sacubic(N);
      figure(3); plot(vE); title('Energy Evolution (E)');
      axis([0 length(vE) 0 105]);
   otherwise,
      disp('Accepted values are (1) or (2)');
   end; % End switch task SAD
case 2,
   disp(sprintf('\n** PART II - ABNET (An AntiBody NETwork) **'));
   L = 10; aux1 = ones(1,L); aux2 = zeros(1,L);
   ag = [aux1;[0,aux1(2:end)];[0,0,aux1(3:end)];[aux1(1:end-1),0];[aux1(1:end-2),0,0];...
         aux2;[1,aux2(2:end)];[1,1,aux2(3:end)];[aux2(1:end-1),1];[aux2(1:end-2),1,1]];
   disp(sprintf('\nAntigens (columnwise) to be Recognized: [%d,%d]',size(ag,1),L)); 
   disp(ag'); ag = ag - 0.1; ag = hardlims(ag);
   eps = input('Choose the Affinity Threshold [0,L]: ');
   [w,win,cwin,D] = abnet(ag,eps);
   figure(2); clf; draw_abnet(w);
case 3,
   disp(sprintf('\n** PART III - CLONALG (The CLONal Selection ALGorithm) **'));
   % figure(1); clf;
   v = cadeia(100,44,0,0,0);
   disp(sprintf('Available Demo Tasks:'));
   disp(sprintf('1) GA (GLOBAL SEARCH)'));
   disp(sprintf('2) CLONALG (MULTI-MODAL OPTIMIZATION)'));
   disp(sprintf('3) CHARACTER (KNOWLEDGE ACQUISITION)'));
   ga_cga = input('Type the desired demo number (1) ,(2) or (3): ');
   switch ga_cga,
   case 1,
      disp(sprintf('** Standard Genetic Algorithm - GA **'));
      figure(1); clf;
      [x,y,fx,vx,vmfit,P] = ga3d(v);
      disp(sprintf('Maximum found [x,y,f(x,y)]: [%.2f,%.2f,%.2f]',x,y,fx));
      figure(2); plot(vx); title('f(x,y) x Mean'); xlabel('Generations'); ylabel('f(x)');
      hold on; plot(vmfit,'r'); hold off;
   case 2,
      disp(sprintf('** Clonal Selection Algorithm - CLONALG **'));
      figure(3); clf; %v = v(1:50,:);
      [x,y,fx,vfx,vmfit,P,vpm] = imalg3d(v);
      save data x y fx vfx;
      disp(sprintf('Maximum found [x,y,f(x,y)]: [%.2f,%.2f,%.2f]',x,y,fx));
      figure(4); clf; plot(vfx); title('f(x,y) x Mean'); xlabel('Generations'); ylabel('f(x)');
      hold on; plot(vmfit,'r'); hold off;
   case 3,
      disp(sprintf('** CLONALG - Knowledge Acquisition**'));
      load num8_12x10; X = v(1,:); P = cadeia(10,120,0,0,0);
      M = imalgchar(P,X);
   otherwise,
      display('Accepted values are (1), (2) or (3)');
   end; % End Switch ga_cga
case 4,
   disp(sprintf('\n** PART IV - AINET (Artificial Immune NETwork) **'));
   disp(sprintf('Available Demo Tasks:'));
   disp(sprintf('1) LINEARLY SEPARABLE CLASSES'));
   disp(sprintf('2) 2-SPIRALS'));
   disp(sprintf('3) CHAINLINK'));
   task = input('Type the desired demo number (1) ,(2) or (3): ');
   switch task,
   case 1,
      load Ag; Ag = norma(Ag);
      figure(1); plotvet(Ag,10); title('Training Patterns');
      ts = input('Choose the suppression threshold (suggested: 0.1): ');
      [M,D] = ainet(Ag,ts,4,10,10,0.2,1);
      [E,Nc,mCe,mC,T] = anal1(M,D);
   case 2,
      figure(1); Ag = spir; title('Training Patterns');
      ts = input('Choose the suppression threshold (suggested: 0.07): ');
      [M,D] = ainet(Ag,ts,4,10,20,0.1,1);
      [E,Nc,mC,T] = anal2(M,D);
      disp(sprintf('Number of clusters: %d',Nc));
   case 3,
      figure(1); Ag = donuts1(500); title('Training Patterns');
      ts = input('Choose the suppression threshold (suggested: 0.2): ');
      [M,D] = ainet(Ag,ts,4,10,5,0.1,1);
      [E,Nc,mC,T] = anal3(M,D);
      disp(sprintf('Number of clusters: %d',Nc));
  otherwise,
      disp('Accepted values are (1), (2) or (3)');
   end; % End Switch task
      
   otherwise,
   disp(sprintf('Accepted values are (1), (2) or (3)'));
   break;
end;
% End Function CHOOSE


% Function DRAW_ABNET
function draw_abnet(w);
[N,L] = size(w);
r = [0.2.*ones(1,N);1:1:N]';
c = [1.2.*ones(1,L);1:1:L]';
if N > L, y = N; else, y = L; end;
plot(r(:,1),r(:,2),'sk'); hold on;
plot(c(:,1),c(:,2),'or');
axis([0 1.4 0 y+1]);
for i = 1:N,
   for j = 1:L,
      if w(i,j) == 1,
         line([r(i,1),c(j,1)],[r(i,2),c(j,2)]);
      end;
   end;
end;
title('ABNET (0 weights ommitted)');
hold off;
% End Function DRAW_ABNET


% Function CADEIA
function [ab,ag] = cadeia(n1,s1,n2,s2,bip)
if nargin == 2,
   n2 = n1; s2 = s1; bip = 1;
elseif nargin == 4,
   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 Plot Vectors
function [] = plotvet(X,nec)
% nec    -> number of elements per class
clf; hold on;
xlabel('x'); ylabel('y');
plot(X(1:nec,1),X(1:nec,2),'bs');
plot(X(nec+1:2*nec,1),X(nec+1:2*nec,2),'ro');
plot(X(2*nec+1:3*nec,1),X(2*nec+1:3*nec,2),'g+');
plot(X(3*nec+1:4*nec,1),X(3*nec+1:4*nec,2),'kx');
plot(X(4*nec+1:5*nec,1),X(4*nec+1:5*nec,2),'mv');
hold off;
% End Function PLOTVET


% Function normalizes matrix over [0,1]
function [Dn] = norma(D);
% Dn  -> normalized vector over [0,1]
[np,ni] = size(D);
if ni == 1,
   Dn = (D - min(D))./(max(D)-min(D));
else,
   vmaxD = max(D); vminD = min(D);
   for i=1:ni,
      Dn(:,i) = (D(:,i) - vminD(i))./(vmaxD(i)-vminD(i));
   end;
end;
% End Function NORMA