ag_basico.m

Diversos ejemplos sobre la aplicacion de algoritmos geneticos.

%% Algoritmo Genetico basico
%==========================
global K1 K2

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% EL ALGORITMO
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
gen = 0;
while (gen <= maxgen),

    gen = gen + 1;
    %% Generacion de una nueva generacion
    NewPop = generar(Pop, Fitness, pcross, pmutation);

    %% Evaluacion de la nueva generacion
Fenotipos = CromToPesos(Pop);
distancias = Fenotipos'*data1; 

clases = orig(3,:)';
bienClasificado = sign(distancias*clases);

[mal,midx]= find(bienClasificado==-1);

Fitness = min(abs(distancias),[],2);
Fitness(midx) = 0;


    %fitness promedio
    FitMax = max(Fitness);
    FitAVG = sum(Fitness)/popsize;
    EvalFitnness(gen,:) = [FitMax; FitAVG];


    Pop = NewPop;
end
%
%% Comportamiento de la poblacion a traves de las
%% generaciones
h2 = figure(2);
plot (EvalFitnness,'*')
axis([1 gen 0 max(EvalFitnness(:))*1.1 ])
title('Capacidad de adaptacion de la poblacion')
xlabel('generacion')
legend('FitMax','FitAVG',4)
%
%% Adaptacion de la poblacion final
[wsort,Ind] = sort(Fitness);
orig = orig';
w = Fenotipos(:,1);
figure(1)
subplot(1,2,2),plot(orig(1:K1,1),orig(1:K1,2),'*',orig(K1+1:K2,1),orig(K1+1:K2,2),'o')
axis('square');grid
v=axis;
[lx,ly]=sline(w,v);
subplot(1,2,1),plot(orig(1:K1,1),orig(1:K1,2),'*',...
    orig(K1+1:K2,1),orig(K1+1:K2,2),'o',lx,ly)
axis('square');grid
title('separating hyperplane')