In this video, I’m going to show you a general concept, matlab code, and one benchmark example of genetic algorithm for solving optimization problems.
This video tutorial was designed for beginners, and I tried to make it as simple as possible. Once you understand this foundation, you can be able to customize and design your own version of genetic algorithm to solve optimization problems in your fields.
Did you know that Genetic algorithm (GA) is one of the most popular stochastic optimization algorithm often used to solve complex large scale optimization problems in various fields.
Here is the general concept of genetic algorithm (Gen & Cheng 1997, pp.1-2):
Genetic Algorithm was first introduced by Holland in 1975, and it is a powerful stochastic search algorithm based on the mechanisms of natural genetics and selection. A general description of genetic algorithm is as follows:
+ Genetic algorithm starts with an initial set of random solutions called population.
+ Each individual in the population is called a chromosome representing a solution to the problem at hand.
+ The chromosomes evolve through successive iterations, called generations.
+ During each generation, the chromosomes are evaluated using some measures of fitness.
+ To create the next generation, new chromosome, called offspring, are formed by either (a) merging two chromosomes from current generation using a crossover operator or (b) modifying a chromosome using a mutation operator.
+ A new generation is formed by (a) selecting, according to the fitness values, some of the parents and offspring and (b) rejecting others so as to keep the population size constant.
+ Fitter chromosomes have higher probabilities of being selected.
+ After several generations, the algorithms converge to the best chromosome, which hopefully represents the optimum or suboptimal solution to the problem.
Let’s see how this genetic algorithm works.
For more videos like this, check my YouTube channel here.
Matlab code
mutation.m
function Y=mutation(P,n)
% P = population
% n = chromosomes to be mutated
[x1 y1]=size(P);
Z=zeros(n,y1);
for i = 1:n
r1=randi(x1);
A1=P(r1,:); % random parent
r2=randi(y1);
if A1(1,r2)== 1
A1(1,r2) = 0; % flick the bit
else
A1(1,r2) = 1;
end
Z(i,:)=A1;
end
Y=Z;
crossover.m
function Y=crossover(P,n)
% P = population
% n = number of pairs of chromosomes to be crossovered
[x1 y1]=size(P);
Z=zeros(2*n,y1);
for i = 1:n
r1=randi(x1,1,2);
while r1(1)==r1(2)
r1=randi(x1,1,2);
end
A1=P(r1(1),:); % parent 1
A2=P(r1(2),:); % parent 2
r2=1+randi(y1-1); % random cutting point
B1=A1(1,r2:y1);
A1(1,r2:y1)=A2(1,r2:40);
A2(1,r2:40)=B1;
Z(2*i-1,:)=A1; % offspring 1
Z(2*i,:)=A2; % offspring 2
end
Y=Z;
population.m
function Y = population(n)
% n = population size
% It is noted that the number of bits to represent the variables
% in binary numbers depends on the required accuracy (the number
% of digits after comma)
% In this example, I want the solution precision with 5 places after the
% decimal point, and with the upper and lower bounds of the variables are 3
% and -3, so, for each variable, we need 20 bits.
% General formula: 2^(m-1) < (upper bound - lower bound)*10^p < 2^m -1
% In this case: p = 5 and m = 20.
% We have 2 variables (x and y), so we need 40 bits in
% total for binary encoding
Y=round(rand(n,40));
evaluation.m
function Y=evaluation(P)
[x1 y1]=size(P);
H=zeros(1,x1);
for i = 1:x1
A=bi2de(P(i,1:y1/2));
x=-3+A*(3-(-3))/(2^(y1/2)-1);
.
.
.
Sorry! This is only a half of the Matlab code.
Notice: It would take you from 1 to 3 hours to re-type the Matlab code yourself; or with just €1.99 (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 €3.99 but today it’s only €1.99 (save €2 today – available for a limited time only)
Download the whole Matlab code here (Membership Code ID: 032)
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!
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View Comments
A survey has been conducted and the data is available in Excel and want to run GA on this data. Can you please guide me how to run GA using this code?
Method 1: For that problem, we can use regression to find the equation representing the relationship between input and output. After that, design GA to solve it. Method 2: Design GA to use only data in Excel (no need regression equation)
Hi Mr.Panda
Thank you for your effort .I want to know what is the type of selection, crossover and mutation you used ? and what are the constraints and optimum of this example?
Hi, they are roulette wheel selection, one cut point crossover, one gene exchange mutation. This is a maximization problem
Solve same like this for linear additional constraints with genetic algorithm
Thanks for the great suggestion!
The run of ga is not clear and if the variables more than 6
For 6 veriables, we need to update the chromosomes, crossover, mutation, and evaluation
In genetic algorithm after run the program Give no feasible solution how to solve this problems please
Check the problem to see if any feasible solution exists, and then check your program.
My problem in genetic algorithm six variables in run Give no feasible point and solution
Penalty function can be used to handle the constraints
How to do it for minimisation problem
Convert min to max. For example: fmax = 1/(1000+fmin)
How to solve genetic algorithm if there is a constraint
For that, we can use penalty functions
I'm struggling with applying Genetic/PSO/ like algo in Feature selection. Please apply them on some dataset and then use classifier to get accuracy. Waiting for code in MATLAB.
Noted. Many thanks for your suggestion!
where to change objective function
In the objective function m file.
I have a five parameters in my proposed method. With the means of different parameter settings, I obtained different accuracy. I want to find the best parameter setting with genetic algorithms. Can you help me in that?
Step 1: find the math function of the objective function. Step 2: math equations of the constraints. Step 3: implement optimization algorithm to solve it. Good luck!
Hi, Dr. Panda. Thank you for your effort. Am very impressed with this tutorial. How to do it for the minimization problem. I have one function with two variables to minimize (1% the tax of mutation and crossover tax between (60%-90%)).
Hi, for that, we can convert the min to max in the objective function.
Thanks for your help
I need to convert higher-order state space(11th) order to second(2nd) order state space in the h-infinity control, anything you may help me with?
Sorry, I can't help with that problem.
Hello sir
please i need your help
I want to optimize a metal structure or I will minimize the weight of the structure
Hi, how can I help?
I want to implement a genetic algorithm in matlab to optimize the weight of a metal gantry and I had some difficulties. If you can give me an example and also I had a problem with the choice of coding.
Thanks
There are several GA codes in Matlab on this blog. You can customize them to solve your problems.
i need to explain functional optimization using GA
.can you suggest which example would be better
I don't know the easier example.