crossover.m

关于时间序列分析的matlab程序代码

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:[e-mail]  (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;