Challenge

Week 4.1 - Genetic Algorithm

UUstad Syed Abdul Quadar Syed Habeeb27 May 2022

Genetic Algorithm theory :

Genetic algorithm is a techniquec to find out the optimal value of the next generation.to perform for find out best solution to next and focuse on the optimization.it is based on darvan theory

they have to convert phonotype to ganotype with the help of encode.

Phenotype means collection of row data.

Geniotype means to convert intezers decimal real number etc.

STEPS:

firstly we use linespace comand to create150 value from 0 to 0.6
we use the mashgraid comand to evalvate the funtion of two variable and these dimantional plots
we use the stalgamite funtin for difrant iteration
we use ga function to deline the stalagamite function for variouse iteration
we mwssure the time for each of 50 iteration and the optimum value is obtained

stalgamite equation

f(x y) = f1,xf2,xf1,yf2,y

f1_x =(sin(5.1*pi*x + 0.5) ).^6;

f1_y = (sin(5.1*pi*y + 0.5)).^6;

f2_x = exp(-4*log(2).* (((x-0.0667).^2)/0.64));

f2_y = exp(-4*log(2).* (((y-0.0667).^2)/0.64));

PROGRAM ON FIND OUT THE STALAGAMITE FUNCTION

%program to cal;culate goloble maximma of stalagamite function

close all

clear all

clc

%difining our surch space

x = linspace(0,0.6,150);

y = linspace (0,0.6,150);

%f = stalagmite_func(input_vector)

%creating a 2dimensional mesh

[xx,yy] = meshgrid (x,y);

num_cases = 50; % number of itteration to run ga i.e ga

%for loop for Evalvating or calling the stalagmite function

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_func(input_vector);

end

%study 1 = statical behavior

%study_time1 = toc;

tic

for i = num_cases

[inputs , fopt(i)] = ga(@stalagmite_func, 2);

xopt(i) = inputs(1);

yopt(i) = inputs(2);

end

study_time1 = toc

%poltting

figure(1)

subplot(2,1,1)

hold on

surfc(x,y ,-f )

hold on

shading interp

plot3 (xopt ,yopt,-fopt,'marker','o','markersize',5,'markerfacecolor','r')

title ('Unbounded input')

subplot(2,1,2)

plot(-fopt)

xlabel('Itertions')

ylabel('Function maximum')

%legend('Itertion','finction minima')

tic

%study 2 = statical behavior with upper and lower bond

for i = num_cases

[inputs , fopt(i)] = ga(@stalagmite_func, 2,[],[],[],[],[0:0],[1:1]);

xopt(i) = inputs(1);

yopt(i) = inputs(2);

end

study_time2 = toc;

figure(2);

subplot(2,1,1);

hold on

surfc(x,y ,f );

hold on

shading interp

plot3 (xopt ,yopt,fopt,'marker','o','markersize',5,'markerfacecolor','r');

title ('Bounded input');

subplot(2,1,2);

plot(fopt);

xlabel('Itertions');

ylabel('Function maximum');

%study 3 study GA ittration

option = optimoptions('ga');

option = optimoptions(option,'populationsize',150);

tic

study_time3 = toc;

for i = num_cases

[inputs , fopt(i)] = ga(@stalagmite_func, 2,[],[],[],[],[0:0],1:1,[],[]);

xopt(i) = inputs(1);

yopt(i) = inputs(2);

end

figure(3)

subplot(2,1,1)

hold on

surfc(x,y ,f )

hold on

shading interp

plot3 (xopt ,yopt,fopt,'marker','o','markersize',5,'markerfacecolor','r')

title ('bounded input with modified population size')

subplot(2,1,2)

plot(fopt)

xlabel('Itertions')

ylabel('Function maximma')

STALAGAMITE FUNCTION CODE:

function [f] = stalagmite_func(input_vector)

x = input_vector(1);

y = input_vector(2);

%f(input) = f1,xf2,xf1,yf2,y

f1_x =(sin(5.1*pi*x + 0.5) ).^6;

f1_y = (sin(5.1*pi*y + 0.5)).^6;

f2_x = exp(-4*log(2).* (((x-0.0667).^2)/0.64));

f2_y = exp(-4*log(2).* (((y-0.0667).^2)/0.64));

f =-f1_x.* f1_y.* f2_x.*f2_y;

end

Week 4.1 - Genetic Algorithm

Ship work like this
in your own course.

Talk to a career counsellor

Ship work like thisin your own course.

Talk to a career counsellor

Ship work like this
in your own course.