www.pudn.com > SGAProToolboxVer.zip > crossover.m

function  [decimal_space_crossover,binary_space_crossover]=crossover(min_confines,max_confines,decimal_space_selected,binary_space_selected,bits,probability_crossover) 
 
%Crossover Function Of Simple Genetic Algorithm Program (Version 1.0.0.1 ) 
%support multi-parameters 
%By chen yi ,CQU .QQ:2376635  Email:cdey@10mail.net  (April,19th,2002) 
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
%Crossover is the step after select in SGA(Simple Genetic Algorithm) 
%by single point cross method 
% 
%min_confines is the minimum of input value in decimal-space 
%max_confines is the maximum of input value in decimal-space 
%~ 
%Usage: 
%new_generation=crossover(decimal_space_rest,probability_crossover) 
%~~ 
%decimal_space_rest is the rest decimal_space from select function 
%~~~ 
%probability_crossover is the crossover probability in crossover step, 
%your can give it's value to reach your need (About:0~1) 
%~~~~ 
%e.g. 
%[binary_space,bits_sum,bits]=coding([1,2,3,4],[7,8,9,10],10,[0.01,0.01,0.01,0.01]) 
%[decimal_space]=decoding([1,2,3,4],[7,8,9,10],binary_space,bits) 
%[fitness_value]=fitness(decimal_space) 
%[max_fitness_temp_position,decimal_space_selected,binary_space_selected,maxfitness]=selection(decimal_space,binary_space,fitness_value,bits) 
%[decimal_space_crossover,binary_space_crossover]=crossover([1,2,3,4],[7,8,9,10],decimal_space_selected,binary_space_selected,bits,0.6) 
 
% 
% 
% See Also  DECODING,CODING ,SELECTION,MUTATION,FITNESS, 
%           FITNESS_FUNCTION, SGA	 
% 
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
 
[population,parameter_numbers]=size(decimal_space_selected); 
% [range_front,range_back] 
[population,bits_sum]=size(binary_space_selected); 
 
probability_pairs_numbers=ceil(probability_crossover*population/2); 
%initialize binary_space_crossover 
binary_space_crossover=binary_space_selected; 
% 
confines_deta=(max_confines-min_confines); 
coded_step=confines_deta./(2.^bits-1); 
%~~~~~~~~~~~~~~~~~~~~~ 
%bits_No is important 
bits_No=[0,bits]; 
% to find out which is the binary parameter 
for i=1:1:parameter_numbers 
   %to avoid that Index exceeds matrix dimensions 
    bits_No(i+1)=bits_No(i+1)+bits_No(i); 
end 
  
%~~~~~~~~~~~~~~~~~~~~~~ 
% 
 if probability_pairs_numbers<1 
    disp('the prabability of crossover is too small, please give another one!!') 
    break; 
 else 
% set crossover points of every parameter      
%to avoid the same sites in colume 1 be used in colume 2 
%to avoid near relation's mating  
%to initialize pairs 
pairs=randint(population/2,2,[1,population]); 
  %test_nonzeros_pairs=nnz(pairs) 
     for i=1:1:population/2 
        while pairs(i,1)==pairs(i,2)|pairs(i,1)==0|pairs(i,2)==0  
              pairs(i,2)=randint(1,1,population); 
              pairs(i,1)=randint(1,1,population); 
        end           
     end 
% avoid the pairs has 0 element 
%while nnz(pairs)~=((population/2).*2) 
%   pairs=randint(population/2,2,[1,population]); 
  
%end 
 
 
     % to choose crossover position from the pairs matrix      
          %crossover_position is the pointer of pairs matrix 
     %initialize  crossover_position 
     crossover_position=randint(probability_pairs_numbers,1,[1,population/2]);    
      
     % to have different position in the next position 
     for i=1:1:probability_pairs_numbers 
         for j=1:1:probability_pairs_numbers 
            while ((i~=j)&(crossover_position(i,1)==crossover_position(j,1)))|crossover_position(j,1)==0 
                   crossover_position(j,1)=randint(1,1,[1,population/2]); 
            end 
         end 
     end 
% to avoid the crossover_position has 0 element      
%     while nnz(crossover_position)~=((probability_pairs_numbers).*1) 
%           crossover_position=randint(probability_pairs_numbers,1,[1,population/2]);    
%     end 
 
  
      % crossover begin 
 
      for i=1:1:probability_pairs_numbers 
          for k=1:1:parameter_numbers 
            mating_point(k)=randint(1,1,[bits_No(k)+1,bits_No(k+1)]); 
            for j=mating_point(k):1:bits_No(k+1) 
               crossover_parts=binary_space_crossover(pairs(crossover_position(i,1),1),j);  
               binary_space_crossover(pairs(crossover_position(i,1),1),j)=binary_space_crossover(pairs(crossover_position(i,1),2),j); 
               binary_space_crossover(pairs(crossover_position(i,1),2),j)=crossover_parts; 
            end   
         end 
          
     end 
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~`~~~~~~~~~~~~~~~~~ 
end 
 
%binary_space_crossover_sparse=sparse(binary_space_crossover);  
% 
 
%~~~~~~~~~~~~~~~~~~~~~~plot~~~~~~~~~~~~~~~~~~~~~` 
% SPY Visualize sparsity pattern. 
% SPY(S) plots the sparsity pattern of the matrix S. 
%figure(5) 
%subplot(1,2,1) 
%spy(binary_space_crossover_sparse,'b'); 
%title('the crossover binary-space in sparsity pattern'); 
%xlabel('Non-Zero bits'); 
%ylabel('Population'); 
%hold on; 
%i=0:0.1:population; 
%for j=1:1:parameter_numbers 
%    plot(bits_No(j+1),i,'r'); 
%end 
 
 
%decimal_space_crossover=decimal_space; 
 
% important ,matrix of matlab begins from 1 ,not 0 
       for i=parameter_numbers:-1:1 
           for j=population:-1:1                       
               for k=bits_No(i+1):-1:(bits_No(i)+1) 
                   twos(j,k)=pow2(bits_No(i+1)-k)*binary_space_crossover(j,k); 
                end  
                twos_sum(j,i)=sum(twos(j,:),2); 
                decimal_space_crossover(j,i)=twos_sum(j,i)*coded_step(i)+min_confines(i); 
           end   
           twos=zeros(population,bits_sum);%clear twos at present i  
           % X=Decimal*coded_step+min_confines 
        end    
         
%~~~~~~~~~~~~plot~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
%figure(6)    
%i=population:-1:1; 
%plot(decimal_space_crossover(i,:),i,'*'); 
%xlabel('crossover decimal-space'); 
%ylabel('population'); 
%title('the crosponding deciaml-space of binary-space'); 
%grid on;