Home > Mateda2.0 > knowledge_extraction > Find_Fitness_Approx.m

# Find_Fitness_Approx

## PURPOSE

[Func_Models] = Find_Fitness_Approx(AllModels,ParetoPop,ParetoVals)

## SYNOPSIS

function[Func_Models] = Find_Fitness_Approx(AllModels,ParetoPop,ParetoVals)

## DESCRIPTION

       [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)

## CROSS-REFERENCE INFORMATION

This function calls:

- BN_Fitness_Corr [bn_fit_corr] = BN_Fitness_Corr(bnet,Pop,FunVal):

This function is called by:

## SOURCE CODE

    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