Challenge

Week 4.1 - Genetic Algorithm

DDhanunjaya Madharapakam21 Jun 2022

Introduction to optimization : It is the process of making something better by providing the initial inputs which then generates the ouputs. The main aim of optimization is to find the points or the set of points in the search space.

Genetic algorithm : The GA works on the principle of evolution theory.In GA's we have a so many possible solutions in terms of population. Then these population undergo the process of natural genetics of producing new

members like childrens and this process is repeated several times for each generation. Each individual in the process have an fitness value based on the function assigned to it and the stronger the fitness value have an higher chances to mate for the next generation, like this for

each generation the the solution is improved efficiently.

Aim : To generate a code to determine the global maxima using GENETIC ALGORITHM in mathlab

code :

1 st iteration

clear all
close all
clc
%inputs
x = linspace(0,0.6,150);
y = linspace(0,0.6,150);
[xx,yy] = meshgrid(x,y);
%creating for loop
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)=stagalmite(input_vector);
end
end
%no of times ga runs
num_cases = 50;
tic
for i=1:num_cases;
[inputs,f_opt(i)]= ga(@stagalmite,2);
x_opt(i)=inputs(1);
y_opt(i)=inputs(2);
opt_f(i)= -f_opt(i);
end
time1=toc
%plotting
subplot(2,1,1)
hold on
surfc(xx,yy,f)
shading interp
plot3(x_opt,y_opt,f_opt,'marker','o','markersize',4,'MarkerFaceColor','r')
title('unbounded inpus')
subplot(2,1,2)
plot(opt_f)
xlabel('iterations')
ylabel('function max')

1.The input parameters are provided.

2. Creating a for loop for the stagalmie function which is inputed.

3.now we are creating for 50 values and we are using the tic toc to represent the time the code ran.

4.The boundaries are unbounded in this case

5.The graphs are plotted and the result is obtained it took about 34 sec to run the code and ouput data

2nd iteration :

clear all
close all
clc

%inputs
x = linspace(0,0.6,150);
y = linspace(0,0.6,150);

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

%creating for loop
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)=stagalmite(input_vector);
    end
end

%no of times ga runs
num_cases = 100;

tic
for i=1:num_cases;
    [inputs,f_opt(i)]= ga(@stagalmite,2,[],[],[],[],[0 0],[0.6,0.6]);
    x_opt(i)=inputs(1);
    y_opt(i)=inputs(2);
    opt_f(i)= -f_opt(i);
end

time1=toc

%plotting
subplot(2,1,1)
hold on
surfc(xx,yy,f)
shading interp
plot3(x_opt,y_opt,f_opt,'marker','o','markersize',4,'markerFaceColor','r')
title('with bounded inpus')
subplot(2,1,2)
plot(opt_f)
xlabel('iterations')
ylabel('function max')

1.The input parameters are provided.

2. Creating a for loop for the stagalmie function which is inputed.

3.now we are creating for 100 values and we are using the tic toc to represent the time the code ran.

4.The boundaries are bounded in this case

5.The graphs are plotted and the result is obtained it took about 100 sec to run the code and ouput data

3rd iteration :

clear all
close all
clc

%inputs
x = linspace(0,0.6,150);
y = linspace(0,0.6,150);

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

%creating for loop
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)=stagalmite(input_vector);
    end
end

%no of times ga runs
num_cases = 150;

options = optimoptions('ga');
options = optimoptions(options,'PopulationSize',450);
tic
for i=1:num_cases;
    [inputs,f_opt(i)]= ga(@stagalmite,2,[],[],[],[],[0 0],[0.6,0.6],[],options);
    x_opt(i)=inputs(1);
    y_opt(i)=inputs(2);
    opt_f(i)= -f_opt(i);
end

time1=toc

%plotting
subplot(2,1,1)
hold on
surfc(xx,yy,f)
shading interp
plot3(x_opt,y_opt,f_opt,'marker','o','markersize',4,'markerFaceColor','r')
title('with bounded inpus')
subplot(2,1,2)
plot(opt_f)
xlabel('iterations')
ylabel('function max')

1.The input parameters are provided.

2. Creating a for loop for the stagalmie function which is inputed.

3.now we are creating for 150 values and we are using the tic toc to represent the time the code ran.

4.The boundaries are bounded in this case

5.The graphs are plotted and the result is obtained it took about 100 sec to run the code and ouput data

Graphs

Results

The required graphs are plotted using the mathlab

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.