genetic_op11.m

function [ f ] = genetic_op( parent_chromosome,chromosomes,adj_mat,edgeslist,pc,pm,V,M )
%GENETIC_OP Summary of this function goes here
[N,m] = size(parent_chromosome);
[N1,m1]=size(chromosomes);
r0=0;
clear m m1
% crossover
p = 1;

for i = 1 : round(N/2) 
    % Select the first parent
    parent_1 = ceil(N*rand(1));  
    r0=rand(1);
  if r0<=0.9
    % Select the second parent
    parent_2 = ceil(N*rand(1));     
    % Make sure both the parents are not the same. 
    while parent_2 == parent_1
        parent_2 = ceil(N*rand(1));
    end
       parent_1 = parent_chromosome(parent_1,:);
       parent_2 = parent_chromosome(parent_2,:);
  else
       parent_2=N+floor((N1-N)*rand(1));
       parent_1 = parent_chromosome(parent_1,:);
       parent_2 = chromosomes(parent_2,:);
  end
    % Get the chromosome information for each randomnly selected parents
   % parent_1 = parent_chromosome(parent_1,:);
   % parent_2 = parent_chromosome(parent_2,:);
    if rand<=pc
        % Perform corssover for each decision variable in the chromosome.
        child_1 = parent_1(1:V);
        child_2 = parent_2(1:V);
        position1 = ceil(V*rand(1));
        position2 = ceil(V*rand(1));
%         while position2 == position1
%             position2 = ceil(V*rand(1));
%         end
        minp = min(position1,position2);
        maxp = max(position1,position2);
        temp = child_1(minp:maxp);
        child_1(minp:maxp) = child_2(minp:maxp);
        child_2(minp:maxp) = temp;
        % Evaluate the objective function for the offsprings and as before
        % concatenate the offspring chromosome with objective value.
        child_1(:,V + 1: M + V) = evaluate_objective(child_1,adj_mat);
        child_2(:,V + 1: M + V) = evaluate_objective(child_2,adj_mat);    
    else
        child_1 = parent_1(1:V+M);
        child_2 = parent_2(1:V+M);  
    end
    f(p,:) = child_1;
    f(p+1,:) = child_2;
    p = p + 2;
end

% mutation
[NN,m] = size(f);
clear m
for i = 1 : NN
    for j = 1:V
        if rand<=pm
            if edgeslist(j).n >= 2
                temp = f(i,j);
                while 1
                    f(i,j) = edgeslist(j).e(ceil(rand*edgeslist(j).n));
                    if temp ~= f(i,j)
                        break;
                    end
                end         
            end     
        end
    end
    f(i,V + 1: M + V) = evaluate_objective(f(i,1:V),adj_mat);
end
end