Find_Fitness_Approx

   [Func_Models] =  Find_Fitness_Approx(AllModels,ParetoPop,ParetoVals)
   Find_Fitness_Approx:   Find the probabilistic model with the highest
                          correlation for each of the objectives in the
                          given Population (e.g. a Pareto set approximation)
                       
 INPUTS 
 AllModels:           Cell array containing the Bayesian networks learned
                      by EDAs b
 ParetoPop:           Population
 ParetoVals:          Evaluation of each of the objectives for all
                      solutions.
 OUTPUTS
 Func_Models: Func_Models{i} is the model with the highest correlation for
              objective i. 

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

0001 function[Func_Models] =  Find_Fitness_Approx(AllModels,ParetoPop,ParetoVals)
0002 %   [Func_Models] =  Find_Fitness_Approx(AllModels,ParetoPop,ParetoVals)
0003 %   Find_Fitness_Approx:   Find the probabilistic model with the highest
0004 %                          correlation for each of the objectives in the
0005 %                          given Population (e.g. a Pareto set approximation)
0006 %
0007 % INPUTS
0008 % AllModels:           Cell array containing the Bayesian networks learned
0009 %                      by EDAs b
0010 % ParetoPop:           Population
0011 % ParetoVals:          Evaluation of each of the objectives for all
0012 %                      solutions.
0013 % OUTPUTS
0014 % Func_Models: Func_Models{i} is the model with the highest correlation for
0015 %              objective i.
0016 %
0017 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
0018 
0019 
0020 nmodels = size(AllModels,2);
0021 n_objectives = size(ParetoVals,2);
0022 
0023 for i=1:nmodels,              % The correlations between the probabilities of each BN and each of
0024   bnet = AllModels{i};        % the problem objectives are computed using the Pareto Set
0025   All_BN_Fit_Corr(i,:) =  BN_Fitness_Corr(bnet,ParetoPop,ParetoVals)
0026 end
0027 
0028 [BestCorr,BestCorrModelsIndex] = max(All_BN_Fit_Corr(i,:)); % The indices of the models with the best correlation are found
0029 
0030 for i=1:n_objectives,   
0031   Func_Models{i} = AllModels{BestCorrModelsIndex(i)};
0032 end,
0033