initialize_variables.m

来自「多目标遗传算法通用编程包」· M 代码 · 共 49 行

function f = initialize_variables(N,problem)

% function f = initialize_variables(N,problem)
% N - Population size
% problem - takes integer values 1 and 2 where,
%           '1' for MOP1
%           '2' for MOP2
%
% This function initializes the population with N individuals and each
% individual having M decision variables based on the selected problem. 
% M = 6 for problem MOP1 and M = 12 for problem MOP2. The objective space
% for MOP1 is 2 dimensional while for MOP2 is 3 dimensional.

%
%     Copyright (C) 2009 Aravind Seshadri
%
%     This program is free software: you can redistribute it and/or modify
%     it under the terms of the GNU General Public License as published by
%     the Free Software Foundation, either version 3 of the License, or
%     (at your option) any later version.
% 
%     This program is distributed in the hope that it will be useful,
%     but WITHOUT ANY WARRANTY; without even the implied warranty of
%     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%     GNU General Public License for more details.
% 
%     You should have received a copy of the GNU General Public License
%     along with this program.  If not, see <http://www.gnu.org/licenses/>.

% Both the MOP's has 0 to 1 as its range for all the decision variables. 
min = 0;
max = 1;
switch problem
    case 1
        M = 6;
        K = 8;
    case 2
        M = 12;
        K = 15;
end
for i = 1 : N
    % Initialize the decision variables
    for j = 1 : M
        f(i,j) = rand(1); % i.e f(i,j) = min + (max - min)*rand(1);
    end
    % Evaluate the objective function
    f(i,M + 1: K) = evaluate_objective(f(i,:),problem);
end