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<html xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <!--This HTML is auto-generated from an M-file.To make changes, update the M-file and republish this document. --> <title>function f = genetic_operator(parent_chromosome, M, V, mu, mum, l_limit, u_limit)</title> <meta name="generator" content="MATLAB 7.0"> <meta name="date" content="2006-03-16"> <meta name="m-file" content="genetic_operator"><style>body { background-color: white; margin:10px;}h1 { color: #990000; font-size: x-large;}h2 { color: #990000; font-size: medium;}p.footer { text-align: right; font-size: xx-small; font-weight: lighter; font-style: italic; color: gray;}pre.codeinput { margin-left: 30px;}span.keyword {color: #0000FF}span.comment {color: #228B22}span.string {color: #A020F0}span.untermstring {color: #B20000}span.syscmd {color: #B28C00}pre.showbuttons { margin-left: 30px; border: solid black 2px; padding: 4px; background: #EBEFF3;}pre.codeoutput { color: gray; font-style: italic;}pre.error { color: red;}/* Make the text shrink to fit narrow windows, but not stretch too far in wide windows. On Gecko-based browsers, the shrink-to-fit doesn't work. */ p,h1,h2,div { /* for MATLAB's browser */ width: 600px; /* for Mozilla, but the "width" tag overrides it anyway */ max-width: 600px; /* for IE */ width:expression(document.body.clientWidth > 620 ? "600px": "auto" );} </style></head> <body> <h1>function f = genetic_operator(parent_chromosome, M, V, mu, mum, l_limit, u_limit)</h1> <p>This function is utilized to produce offsprings from parent chromosomes. The genetic operators corssover and mutation which are carried out with slight modifications from the original design. For more information read the document enclosed. </p> <p>parent_chromosome - the set of selected chromosomes. M - number of objective functions V - number of decision varaiables mu - distribution index for crossover (read the enlcosed pdf file) mum - distribution index for mutation (read the enclosed pdf file) l_limit - a vector of lower limit for the corresponding decsion variables u_limit - a vector of upper limit for the corresponding decsion variables </p> <p>The genetic operation is performed only on the decision variables, that is the first V elements in the chromosome vector.</p><pre class="codeinput">[N,m] = size(parent_chromosome);clear <span class="string">m</span>p = 1;<span class="comment">% Flags used to set if crossover and mutation were actually performed.</span>was_crossover = 0;was_mutation = 0;<span class="keyword">for</span> i = 1 : N <span class="comment">% With 90 % probability perform crossover</span> <span class="keyword">if</span> rand(1) < 0.9 <span class="comment">% Initialize the children to be null vector.</span> child_1 = []; child_2 = []; <span class="comment">% Select the first parent</span> parent_1 = round(N*rand(1)); <span class="keyword">if</span> parent_1 < 1 parent_1 = 1; <span class="keyword">end</span> <span class="comment">% Select the second parent</span> parent_2 = round(N*rand(1)); <span class="keyword">if</span> parent_2 < 1 parent_2 = 1; <span class="keyword">end</span> <span class="comment">% Make sure both the parents are not the same.</span> <span class="keyword">while</span> isequal(parent_chromosome(parent_1,:),parent_chromosome(parent_2,:)) parent_2 = round(N*rand(1)); <span class="keyword">if</span> parent_2 < 1 parent_2 = 1; <span class="keyword">end</span> <span class="keyword">end</span> <span class="comment">% Get the chromosome information for each randomnly selected</span> <span class="comment">% parents</span> parent_1 = parent_chromosome(parent_1,:); parent_2 = parent_chromosome(parent_2,:); <span class="comment">% Perform corssover for each decision variable in the chromosome.</span> <span class="keyword">for</span> j = 1 : V <span class="comment">% SBX (Simulated Binary Crossover).</span> <span class="comment">% For more information about SBX refer the enclosed pdf file.</span> <span class="comment">% Generate a random number</span> u(j) = rand(1); <span class="keyword">if</span> u(j) <= 0.5 bq(j) = (2*u(j))^(1/(mu+1)); <span class="keyword">else</span> bq(j) = (1/(2*(1 - u(j))))^(1/(mu+1)); <span class="keyword">end</span> <span class="comment">% Generate the jth element of first child</span> child_1(j) = <span class="keyword">...</span> 0.5*(((1 + bq(j))*parent_1(j)) + (1 - bq(j))*parent_2(j)); <span class="comment">% Generate the jth element of second child</span> child_2(j) = <span class="keyword">...</span> 0.5*(((1 - bq(j))*parent_1(j)) + (1 + bq(j))*parent_2(j)); <span class="comment">% Make sure that the generated element is within the specified</span> <span class="comment">% decision space else set it to the appropriate extrema.</span> <span class="keyword">if</span> child_1(j) > u_limit(j) child_1(j) = u_limit(j); <span class="keyword">elseif</span> child_1(j) < l_limit(j) child_1(j) = l_limit(j); <span class="keyword">end</span> <span class="keyword">if</span> child_2(j) > u_limit(j) child_2(j) = u_limit(j); <span class="keyword">elseif</span> child_2(j) < l_limit(j) child_2(j) = l_limit(j); <span class="keyword">end</span> <span class="keyword">end</span> <span class="comment">% Evaluate the objective function for the offsprings and as before</span> <span class="comment">% concatenate the offspring chromosome with objective value.</span> child_1(:,V + 1: M + V) = evaluate_objective(child_1, M, V); child_2(:,V + 1: M + V) = evaluate_objective(child_2, M, V); <span class="comment">% Set the crossover flag. When crossover is performed two children</span> <span class="comment">% are generate, while when mutation is performed only only child is</span> <span class="comment">% generated.</span> was_crossover = 1; was_mutation = 0; <span class="comment">% With 10 % probability perform mutation. Mutation is based on</span> <span class="comment">% polynomial mutation.</span> <span class="keyword">else</span> <span class="comment">% Select at random the parent.</span> parent_3 = round(N*rand(1)); <span class="keyword">if</span> parent_3 < 1 parent_3 = 1; <span class="keyword">end</span> <span class="comment">% Get the chromosome information for the randomnly selected parent.</span> child_3 = parent_chromosome(parent_3,:); <span class="comment">% Perform mutation on eact element of the selected parent.</span> <span class="keyword">for</span> j = 1 : V r(j) = rand(1); <span class="keyword">if</span> r(j) < 0.5 delta(j) = (2*r(j))^(1/(mum+1)) - 1; <span class="keyword">else</span> delta(j) = 1 - (2*(1 - r(j)))^(1/(mum+1)); <span class="keyword">end</span> <span class="comment">% Generate the corresponding child element.</span> child_3(j) = child_3(j) + delta(j); <span class="comment">% Make sure that the generated element is within the decision</span> <span class="comment">% space.</span> <span class="keyword">if</span> child_3(j) > u_limit(j) child_3(j) = u_limit(j); <span class="keyword">elseif</span> child_3(j) < l_limit(j) child_3(j) = l_limit(j); <span class="keyword">end</span> <span class="keyword">end</span> <span class="comment">% Evaluate the objective function for the offspring and as before</span> <span class="comment">% concatenate the offspring chromosome with objective value.</span> child_3(:,V + 1: M + V) = evaluate_objective(child_3, M, V); <span class="comment">% Set the mutation flag</span> was_mutation = 1; was_crossover = 0; <span class="keyword">end</span> <span class="comment">% Keep proper count and appropriately fill the child variable with all</span> <span class="comment">% the generated children for the particular generation.</span> <span class="keyword">if</span> was_crossover child(p,:) = child_1; child(p+1,:) = child_2; was_cossover = 0; p = p + 2; <span class="keyword">elseif</span> was_mutation child(p,:) = child_3(1,1 : M + V); was_mutation = 0; p = p + 1; <span class="keyword">end</span><span class="keyword">end</span>f = child;</pre><p class="footer"><br> Published with MATLAB® 7.0<br></p> <!--##### SOURCE BEGIN #####
%% function f = genetic_operator(parent_chromosome, M, V, mu, mum, l_limit, u_limit)
%
% This function is utilized to produce offsprings from parent chromosomes.
% The genetic operators corssover and mutation which are carried out with
% slight modifications from the original design. For more information read
% the document enclosed.
%
% parent_chromosome - the set of selected chromosomes.
% M - number of objective functions
% V - number of decision varaiables
% mu - distribution index for crossover (read the enlcosed pdf file)
% mum - distribution index for mutation (read the enclosed pdf file)
% l_limit - a vector of lower limit for the corresponding decsion variables
% u_limit - a vector of upper limit for the corresponding decsion variables
%
% The genetic operation is performed only on the decision variables, that
% is the first V elements in the chromosome vector.
[N,m] = size(parent_chromosome);
clear m
p = 1;
% Flags used to set if crossover and mutation were actually performed.
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
% 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, M, V);
child_2(:,V + 1: M + V) = evaluate_objective(child_2, M, V);
% Set the crossover flag. When crossover is performed two children
% are generate, while when mutation is performed only only child is
% generated.
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
% Evaluate the objective function for the offspring and as before
% concatenate the offspring chromosome with objective value.
child_3(:,V + 1: M + V) = evaluate_objective(child_3, M, V);
% Set the mutation flag
was_mutation = 1;
was_crossover = 0;
end
% Keep proper count and appropriately fill the child variable with all
% the generated children for the particular generation.
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;##### SOURCE END #####--> </body></html>⌨️ 快捷键说明
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