sele_genet.m

Genetic Algorithm + Neural Network for caracteristic optimization

function x=sele_genet(dir);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% OP钦ES DO ALGORITMO GEN蒚ICO
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Popsize=30;
tam_cro=20;
rand('twister' ,110);
randn('state' ,110);
options = gaoptimset;
Mat =uint8(logical(0.5 > rand(Popsize,tam_cro)));  %initial population

options = gaoptimset(options,'PopulationSize',Popsize);
options = gaoptimset(options,'InitialPopulation',Mat); %define the initial population
options = gaoptimset(options,'Generations',50);
options = gaoptimset(options,'StallGenLimit' ,50);
%options = gaoptimset(options,'FitnessScalingFcn',@fitscalingtop);
options = gaoptimset(options,'PopulationType' ,'bitString');
options = gaoptimset(options,'SelectionFcn' ,@selectionroulette);
%options = gaoptimset(options,'SelectionFcn' ,{@selectiontournament 4});
%options = gaoptimset(options,'CrossoverFcn' , @crossovertwopoint);
options = gaoptimset(options,'CrossoverFcn' , @crossoversinglepoint);
options = gaoptimset(options,'CrossoverFraction',0.8);
options = gaoptimset(options,'MutationFcn' ,{@mutationuniform 0.2 });
options = gaoptimset(options,'EliteCount' ,1);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% IMPORTA O VETOR DE CARACTER蚐TICAS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
rawData1 = importdata(dir);
C=rawData1;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% EXECUTA O ALGORITMO GEN蒚ICO
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Fitness_Function = @(x) fun_custo_nn(x,C);
[x,fval,reason,output,population,scores] =  ga(Fitness_Function,tam_cro,[],[],[],[],[],[],[],options)
fid = fopen('coor_res.txt', 'wt');
fprintf(fid, '%d ', x);
fclose(fid)