Dr.PandaIn this video, I will show you a Matlab code and test the performance of a robust genetic algorithm which can solve global optimization problems with very high success rate.
You can use this GA code in Matlab in your research work. Let’s see.
For more videos like this, check my YouTube channel here.
population.m
function Y = population(n)
% n = population size
% 40 = number of bits to express the number. Why? It depends on
% the required accuracy (the number of digits after comma) that we want
Y=round(rand(n,40));
crossover.m
function Y=crossover(P,n)
% P = population
% n = pair of chromosomes to be crossovered
[x1 y1]=size(P);
Z=zeros(2*n,40);
for i = 1:n
r1=randi(x1,1,2);
while r1(1)==r1(2)
r1=randi(x1,1,2);
end
A1=P(r1(1),:);
A2=P(r1(2),:);
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mutation.m
function Y=mutation(P,n)
% P = population
% n = chromosomes to be mutated
[x1 y1]=size(P);
Z=zeros(n,40);
for i = 1:n
r1=randi(x1);
A1=P(r1,:);
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evaluation.m
function Y=evaluation(P)
[x1 y1]=size(P);
H=zeros(x1,1);
for i = 1:x1
A=bi2de(P(i,1:20));
x=-40+A*(40-(-40))/(2^20-1);
B=bi2de(P(i,21:40));
y=-40+B*(40-(-40))/(2^20-1);
H(i,1)= 0.4/(1+0.02*((x-(-20)).^2 +(y-(-20)).^2))...
+ 0.2/(1+0.5*((x-(-5)).^2 +(y-(-25)).^2))...
+ 0.7/(1+0.01*((x-(0)).^2 +(y-(30)).^2))...
+ 1/(1+2*((x-(30)).^2 +(y-(0)).^2))...
+ 0.05/(1+0.1*((x-(30)).^2 +(y-(-30)).^2));
end
Y=H;
selection.m
function [YY1 YY2] = selection(P1,B,p,s)
% P1 - population
% B - fitness value
% p - population size
% s = select top s chromsomes
%-------------------------------------------------------------------------
% Top selection operation
B=B';
for i =1:s
[r1 c1]=find(B==max(B));
Y1(i,:)=P1(max(c1),:);
Fn(i)=B(max(c1));
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GA.m
% Main Code
clear all
clc
close all
tic
%--------------------------------------------------------------------------
% Population size
p =60;
% Number of pairs of chromosomes required for crossover
c=5;
% Number of chromosomes required for mutation
m=50;
% Adaptive stopping = no. of generations not improve quality
s=2;
% Number of chromosomes passing between generation
g=3;
% Number of chromosomes passing between runs
r=10;
%-------------------------------------------------------------------
% Computing time (s)
t = 10;
tg=1000000; % total number of generations (tg). We set tg very large to
% ensure that the GA will be stopped by computing time (t)
%--------------------------------------------------------------------------
P1=population(r); % Initial guess
w=1;
for j = 1:tg
P=population(p-r);
P(p-r+1:p,:)=P1;
for i=1:tg
% Extended population
P(p+1:p+2*c,:)=crossover(P,c);
P(p+2*c+1:p+2*c+m,:)=mutation(P,m);
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Sorry! This is only a half of the code.
Notice: It’s possible to watch the video and re-type the Matlab code yourself – that would take you from 1 to 3 hours; or with just €2.99 (the cost of a cup of coffee), you can download/copy the whole Matlab code within 2 minutes. It’s your choice to make.
Original price is €4.99 but today it’s only €2.99 (save €2 today – available for a limited time only)
Download the whole Matlab code here (Membership Code ID: 007)
No need to build the Matlab code from scratch because it’s very time-consuming. My idol, Jim Rohn, once said: “Time is more value than money. You can get more money, but you cannot get more time”. If you think this code can be used in your research/teaching work, you should download it and then customize/modify/apply it to your work, without any obligation of citing the original source if you don’t want. However, redistribution (i.e., downloading the code/script here and then making it available on another site on the Internet) is strictly prohibited.
If you have any question or problem, please contact Dr. Panda by email: learnwithpanda2018@gmail.com
Thank you very much and good luck with your research!