MixtureGaussianEDAs_OfflineHP

 EXAMPLE 14:   Mixtures of full Gaussian distributions
               for  the Offline HP Model continuous function (see
               previous examples for explanation on the Offline HP Model)

0001  % EXAMPLE 14:   Mixtures of full Gaussian distributions
0002  %               for  the Offline HP Model continuous function (see
0003  %               previous examples for explanation on the Offline HP Model)
0004  
0005  
0006  Fibbonacci_n = 9; % Fibbonacci_n: Value n for the construction of the Fibbonacci sequence. NumbVar = F(n)
0007  global HPInitConf;
0008  HPInitConf = CreateFibbInitConf(Fibbonacci_n); % HP Fibbonacci configuration
0009  NumbVar = size(HPInitConf,2);
0010  PopSize = 200; 
0011  F = 'EvaluateOffHPProtein';
0012  cache  = [1,1,1,1,1]; Card = [zeros(1,NumbVar);2*pi*ones(1,NumbVar)];
0013  learning_params(1:5) = {'vars','ClusterPointsKmeans',5,'sqEuclidean',1};
0014  edaparams{1} = {'learning_method','LearnMixtureofFullGaussianModels',learning_params};
0015  edaparams{2} = {'sampling_method','SampleMixtureofFullGaussianModels',{PopSize,1}};
0016  edaparams{3} = {'replacement_method','best_elitism',{'fitness_ordering'}};
0017  selparams(1:2) = {0.5,'fitness_ordering'};
0018  edaparams{4} = {'selection_method','truncation_selection',selparams};
0019  edaparams{5} = {'repairing_method',' Trigom_repairing',{}};
0020  edaparams{6} = {'stop_cond_method','max_gen',{100}};
0021  edaparams{7} = {'local_opt_method','local_search_OffHP',{}};
0022   [AllStat,Cache]=RunEDA(PopSize,NumbVar,F,Card,cache,edaparams) 
0023  % To draw the resulting solution use function OffPrintProtein(vector),
0024  % where vector is the best solution found.