prop_selection

 [SelPop,SelFunVal]= prop_selection(Pop,FunVal,selection_params)

 prop_selection:       Proportional selection for single objective functions
 INPUTS 
 Pop:                          Original population
 FunVal:                       A matrix of function evaluations, one vector of m objectives for each individual
 OUTPUTS
 SelPop: Selected population
 SelFunVal:  A vector of function evaluations for each selected individual

 Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)

0001 function[SelPop,SelFunVal]= prop_selection(Pop,FunVal,selection_params)
0002 % [SelPop,SelFunVal]= prop_selection(Pop,FunVal,selection_params)
0003 %
0004 % prop_selection:       Proportional selection for single objective functions
0005 % INPUTS
0006 % Pop:                          Original population
0007 % FunVal:                       A matrix of function evaluations, one vector of m objectives for each individual
0008 % OUTPUTS
0009 % SelPop: Selected population
0010 % SelFunVal:  A vector of function evaluations for each selected individual
0011 %
0012 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
0013  
0014    PopSize = size(Pop,1);
0015    
0016    minval = min(FunVal)+10^(-60);
0017    partialsum = (FunVal+minval)/sum(FunVal+minval);  
0018                                            % minval is used to guarantee a positive selection
0019                                            % probability to individuals
0020                                            % with negative fitness values
0021    partialsum=cumsum(partialsum);
0022    Index=sus(PopSize,partialsum);          % Stochastic Universal Sampling is used for sampling individuals
0023    SelPop=Pop(Index,:);                   
0024    SelFunVal=FunVal(Index,:);  
0025    
0026    return
0027  
0028  
0029