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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,pro,mu,mum);</title> <meta name="generator" content="MATLAB 7.0"> <meta name="date" content="2006-03-07"> <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,pro,mu,mum);</h1><pre class="codeinput"><span class="comment">% This function is utilized to produce offsprings from parent chromosomes.</span><span class="comment">% The genetic operators corssover and mutation which are carried out with</span><span class="comment">% slight modifications from the original design. For more information read</span><span class="comment">% the document enclosed.</span>[N,M] = size(parent_chromosome);<span class="keyword">switch</span> pro <span class="keyword">case</span> 1 M = 2; V = 6; <span class="keyword">case</span> 2 M = 3; V = 12;<span class="keyword">end</span>p = 1;was_crossover = 0;was_mutation = 0;l_limit = 0;u_limit = 1;<span class="keyword">for</span> i = 1 : N <span class="keyword">if</span> rand(1) < 0.9 child_1 = []; child_2 = []; parent_1 = round(N*rand(1)); <span class="keyword">if</span> parent_1 < 1 parent_1 = 1; <span class="keyword">end</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="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> parent_1 = parent_chromosome(parent_1,:); parent_2 = parent_chromosome(parent_2,:); <span class="keyword">for</span> j = 1 : V <span class="comment">% SBX (Simulated Binary Crossover)</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> child_1(j) = <span class="keyword">...</span> 0.5*(((1 + bq(j))*parent_1(j)) + (1 - bq(j))*parent_2(j)); child_2(j) = <span class="keyword">...</span> 0.5*(((1 - bq(j))*parent_1(j)) + (1 + bq(j))*parent_2(j)); <span class="keyword">if</span> child_1(j) > u_limit child_1(j) = u_limit; <span class="keyword">elseif</span> child_1(j) < l_limit child_1(j) = l_limit; <span class="keyword">end</span> <span class="keyword">if</span> child_2(j) > u_limit child_2(j) = u_limit; <span class="keyword">elseif</span> child_2(j) < l_limit child_2(j) = l_limit; <span class="keyword">end</span> <span class="keyword">end</span> child_1(:,V + 1: M + V) = evaluate_objective(child_1,pro); child_2(:,V + 1: M + V) = evaluate_objective(child_2,pro); was_crossover = 1; was_mutation = 0; <span class="keyword">else</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">% Make sure that the mutation does not result in variables out of</span> <span class="comment">% the search space. For both the MOP's the range for decision space</span> <span class="comment">% is [0,1]. In case different variables have different decision</span> <span class="comment">% space each variable can be assigned a range.</span> child_3 = parent_chromosome(parent_3,:); <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> child_3(j) = child_3(j) + delta(j); <span class="keyword">if</span> child_3(j) > u_limit child_3(j) = u_limit; <span class="keyword">elseif</span> child_3(j) < l_limit child_3(j) = l_limit; <span class="keyword">end</span> <span class="keyword">end</span> child_3(:,V + 1: M + V) = evaluate_objective(child_3,pro); was_mutation = 1; was_crossover = 0; <span class="keyword">end</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,pro,mu,mum);
% 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.
[N,M] = size(parent_chromosome);
switch pro
case 1
M = 2;
V = 6;
case 2
M = 3;
V = 12;
end
p = 1;
was_crossover = 0;
was_mutation = 0;
l_limit = 0;
u_limit = 1;
for i = 1 : N
if rand(1) < 0.9
child_1 = [];
child_2 = [];
parent_1 = round(N*rand(1));
if parent_1 < 1
parent_1 = 1;
end
parent_2 = round(N*rand(1));
if parent_2 < 1
parent_2 = 1;
end
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
parent_1 = parent_chromosome(parent_1,:);
parent_2 = parent_chromosome(parent_2,:);
for j = 1 : V
% SBX (Simulated Binary Crossover)
% 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
child_1(j) = ...
0.5*(((1 + bq(j))*parent_1(j)) + (1 - bq(j))*parent_2(j));
child_2(j) = ...
0.5*(((1 - bq(j))*parent_1(j)) + (1 + bq(j))*parent_2(j));
if child_1(j) > u_limit
child_1(j) = u_limit;
elseif child_1(j) < l_limit
child_1(j) = l_limit;
end
if child_2(j) > u_limit
child_2(j) = u_limit;
elseif child_2(j) < l_limit
child_2(j) = l_limit;
end
end
child_1(:,V + 1: M + V) = evaluate_objective(child_1,pro);
child_2(:,V + 1: M + V) = evaluate_objective(child_2,pro);
was_crossover = 1;
was_mutation = 0;
else
parent_3 = round(N*rand(1));
if parent_3 < 1
parent_3 = 1;
end
% Make sure that the mutation does not result in variables out of
% the search space. For both the MOP's the range for decision space
% is [0,1]. In case different variables have different decision
% space each variable can be assigned a range.
child_3 = parent_chromosome(parent_3,:);
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
child_3(j) = child_3(j) + delta(j);
if child_3(j) > u_limit
child_3(j) = u_limit;
elseif child_3(j) < l_limit
child_3(j) = l_limit;
end
end
child_3(:,V + 1: M + V) = evaluate_objective(child_3,pro);
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;##### SOURCE END #####--> </body></html>?? 快捷鍵說明
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