Genetic algorithm

clear all
close all
clc

%dividing x,y into 150 parts between the range 0 to 0.6
x=linspace(0,0.6,150);
y=linspace(0,0.6,150);
cases=50; %total cases =50
[xx yy]= meshgrid(x,y);

for i=1:length(xx)
    for j=1:length(yy)
        input_vector(1)=xx(i,j);
        input_vector(2)=yy(i,j);
        f(i,j)=stalagmite(input_vector);
    end
end


options=gaoptimset(@ga);%intializing options setting for GA
options=gaoptimset(options,\'PopulationSize\',200);%setting population size to 200

tic
for i= 1:cases
    [inputs,fopt(i)]=ga(@stalagmite,2,[],[],[],[],[0;0],[1;1],[],[],options);
    %(function,no_of_variables,linear inequalities,linear equalities,bounds,nonlinear constraint func,interger indices,opts)
    xopt(i)=inputs(1);
    yopt(i)=inputs(2);
end

time=toc
figure(1)
subplot(2,1,1)
hold on
surfc(x,y,f)%plotting the surfaces
shading interp
plot3(xopt,yopt,fopt,\'marker\',\'o\')%ploting min values for each case with marker o
title(\'Bounded inputs\')
subplot(2,1,2)
plot(fopt) 
xlabel(\'iterations\');
ylabel(\'Min. func\');
%since GA finds only minimum value, we did fuction=-(function) to inverse
%the plot. but now the maximum value is around 0.99995 we assume.

function [q]=stalagmite(qw)
f1=(sin(5.1*pi*qw(1)+0.5)).^6;
f2=(sin(5.1*pi*qw(2)+0.5)).^6;
f3=exp(-4*log(2)*(qw(1)-0.0667)^2/0.64);
f4=exp(-4*log(2)*(qw(2)-0.0667)^2/0.64);
q=f1*f2*f3*f4;
q=-q;
end

the answer is 0.99995.

the iterations are more variying because of more accuracy of the value (0.0000)

if it was 0.999, all values are the same only