function f = genetic_operator(parent_chromosome, M, V, mu, mum, l_limit, u_limit)
%遗传算子
[N,m] = size(parent_chromosome);
clear m
p = 1;
was_crossover = 0;
was_mutation = 0;
for i = 1 : N
% With 90 % probability perform crossover
if rand(1) < 0.9
% Initialize the children to be null vector.
child_1 = [];
child_2 = [];
% Select the first parent
parent_1 = round(N*rand(1));
if parent_1 < 1
parent_1 = 1;
end
% Select the second parent
parent_2 = round(N*rand(1));
if parent_2 < 1
parent_2 = 1;
end
% Make sure both the parents are not the same.
while isequal(parent_chromosome(parent_1,:),parent_chromosome(parent_2,:))
parent_2 = round(N*rand(1));
if parent_2 < 1
parent_2 = 1;
end
end
% Get the chromosome information for each randomnly selected
% parents
parent_1 = parent_chromosome(parent_1,:);
parent_2 = parent_chromosome(parent_2,:);
% Perform corssover for each decision variable in the chromosome.
for j = 1 : V
% SBX (Simulated Binary Crossover).
% For more information about SBX refer the enclosed pdf file.
% Generate a random number
u(j) = rand(1);
if u(j) <= 0.5
bq(j) = (2*u(j))^(1/(mu+1));
else
bq(j) = (1/(2*(1 - u(j))))^(1/(mu+1));
end
% Generate the jth element of first child
child_1(j) = ...
0.5*(((1 + bq(j))*parent_1(j)) + (1 - bq(j))*parent_2(j));
% Generate the jth element of second child
child_2(j) = ...
0.5*(((1 - bq(j))*parent_1(j)) + (1 + bq(j))*parent_2(j));
% Make sure that the generated element is within the specified
% decision space else set it to the appropriate extrema.
if child_1(j) > u_limit(j)
child_1(j) = u_limit(j);
elseif child_1(j) < l_limit(j)
child_1(j) = l_limit(j);
end
if child_2(j) > u_limit(j)
child_2(j) = u_limit(j);
elseif child_2(j) < l_limit(j)
child_2(j) = l_limit(j);
end
end
child_1(:,V + 1: M + V) = evaluate_objective(child_1, M, V);
child_2(:,V + 1: M + V) = evaluate_objective(child_2, M, V);
was_crossover = 1;
was_mutation = 0;
% With 10 % probability perform mutation. Mutation is based on
% polynomial mutation.
else
% Select at random the parent.
parent_3 = round(N*rand(1));
if parent_3 < 1
parent_3 = 1;
end
% Get the chromosome information for the randomnly selected parent.
child_3 = parent_chromosome(parent_3,:);
% Perform mutation on eact element of the selected parent.
for j = 1 : V
r(j) = rand(1);
if r(j) < 0.5
delta(j) = (2*r(j))^(1/(mum+1)) - 1;
else
delta(j) = 1 - (2*(1 - r(j)))^(1/(mum+1));
end
% Generate the corresponding child element.
child_3(j) = child_3(j) + delta(j);
% Make sure that the generated element is within the decision
% space.
if child_3(j) > u_limit(j)
child_3(j) = u_limit(j);
elseif child_3(j) < l_limit(j)
child_3(j) = l_limit(j);
end
end
child_3(:,V + 1: M + V) = evaluate_objective(child_3, M, V);
% Set the mutation flag
was_mutation = 1;
was_crossover = 0;
end
if was_crossover
child(p,:) = child_1;
child(p+1,:) = child_2;
was_cossover = 0;
p = p + 2;
elseif was_mutation
child(p,:) = child_3(1,1 : M + V);
was_mutation = 0;
p = p + 1;
end
end
f = child;