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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>initialize_variables</title>      <meta name="generator" content="MATLAB 7.0">      <meta name="date" content="2006-03-16">      <meta name="m-file" content="initialize_variables"><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>      <h2>Contents</h2>      <div>         <ul>            <li><a href="#1">function f = initialize_variables(N, M, V, min_tange, max_range)</a></li>            <li><a href="#2">Initialize each chromosome</a></li>         </ul>      </div>      <h2>function f = initialize_variables(N, M, V, min_tange, max_range)<a name="1"></a></h2>      <p>This function initializes the chromosomes. Each chromosome has the following at this stage       * set of decision variables               * objective function values      </p>      <p>where, N - Population size M - Number of objective functions V - Number of decision variables min_range - A vector of decimal         values which indicate the minimum value for each decision variable. max_range - Vector of maximum possible values for decision         variables.      </p><pre class="codeinput">min = min_range;max = max_range;<span class="comment">% K is the total number of array elements. For ease of computation decision</span><span class="comment">% variables and objective functions are concatenated to form a single</span><span class="comment">% array. For crossover and mutation only the decision variables are used</span><span class="comment">% while for selection, only the objective variable are utilized.</span>K = M + V;</pre><h2>Initialize each chromosome<a name="2"></a></h2>      <p>For each chromosome perform the following (N is the population size)</p><pre class="codeinput"><span class="keyword">for</span> i = 1 : N    <span class="comment">% Initialize the decision variables based on the minimum and maximum</span>    <span class="comment">% possible values. V is the number of decision variable. A random</span>    <span class="comment">% number is picked between the minimum and maximum possible values for</span>    <span class="comment">% the each decision variable.</span>    <span class="keyword">for</span> j = 1 : V        f(i,j) = min(j) + (max(j) - min(j))*rand(1);    <span class="keyword">end</span>    <span class="comment">% For ease of computation and handling data the chromosome also has the</span>    <span class="comment">% vlaue of the objective function concatenated at the end. The elements</span>    <span class="comment">% V + 1 to K has the objective function valued.</span>    <span class="comment">% The function evaluate_objective takes one chromosome at a time,</span>    <span class="comment">% infact only the decision variables are passed to the function along</span>    <span class="comment">% with information about the number of objective functions which are</span>    <span class="comment">% processed and returns the value for the objective functions. These</span>    <span class="comment">% values are now stored at the end of the chromosome itself.</span>    f(i,V + 1: K) = evaluate_objective(f(i,:), M, V);<span class="keyword">end</span></pre><p class="footer"><br>         Published with MATLAB&reg; 7.0<br></p>      <!--##### SOURCE BEGIN #####

%% function f = initialize_variables(N, M, V, min_tange, max_range) 
% This function initializes the chromosomes. Each chromosome has the
% following at this stage
%       * set of decision variables
%       * objective function values
% 
% where,
% N - Population size
% M - Number of objective functions
% V - Number of decision variables
% min_range - A vector of decimal values which indicate the minimum value
% for each decision variable.
% max_range - Vector of maximum possible values for decision variables.

min = min_range;
max = max_range;

% K is the total number of array elements. For ease of computation decision
% variables and objective functions are concatenated to form a single
% array. For crossover and mutation only the decision variables are used
% while for selection, only the objective variable are utilized.

K = M + V;

%% Initialize each chromosome
% For each chromosome perform the following (N is the population size)
for i = 1 : N
    % Initialize the decision variables based on the minimum and maximum
    % possible values. V is the number of decision variable. A random
    % number is picked between the minimum and maximum possible values for
    % the each decision variable.
    for j = 1 : V
        f(i,j) = min(j) + (max(j) - min(j))*rand(1);
    end
    % For ease of computation and handling data the chromosome also has the
    % vlaue of the objective function concatenated at the end. The elements
    % V + 1 to K has the objective function valued. 
    % The function evaluate_objective takes one chromosome at a time,
    % infact only the decision variables are passed to the function along
    % with information about the number of objective functions which are
    % processed and returns the value for the objective functions. These
    % values are now stored at the end of the chromosome itself.
    f(i,V + 1: K) = evaluate_objective(f(i,:), M, V);
end##### SOURCE END #####-->   </body></html>

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