The way the GA will be coded will depend on your problem. What represents chromosomes in your problem is not general. Having said that, there are some examples here:

https://www.mathworks.com/matlabcentral/fileexchange/15164-speedyga-a-fast-simple-genetic-algorithm

https://www.mathworks.com/matlabcentral/fileexchange/24053-turboga-a-simple-genetic-algorithm-with-a-powerful-performance-enhancing-tweak

https://www.mathworks.com/matlabcentral/fileexchange/39021-basic-genetic-algorithm

https://www.mathworks.com/matlabcentral/fileexchange/67435-the-genetic-algorithm-ga-selection-crossover-mutation-elitism

https://www.mathworks.com/matlabcentral/fileexchange/19780-genetic-algorithm

https://www.mathworks.com/matlabcentral/fileexchange/34144-simple-example-of-genetic-algorithm-for-optimization-problems

Find the minimum function using genetic algorithm

https://www.mathworks.com/help/gads/ga.html

And when are you going to use math prog tools - which WILL provide you with an OPTIMUM? You are wasting your time with these tools.

What is the problem that you are studying? As Michael Patriksson also indicated, you may perhaps be able to use exact optimization methods to solve your research problem.

Hi! I have this code to a hybrid meta-heuristic (population and trajectory).

https://www.mathworks.com/matlabcentral/fileexchange/84732-lichtenberg-algorithm-la

Michael and Crenk sir I have considered the problem in interval environment further the function is extremely non-linear and for me it is difficult to solve using analytical optimization theory so I want to use three different algorithms CPSO , WPSO and GA and by comparing the results I will conclude the solution part.

please refer this

https://www.mathworks.com/help/gads/ga.html

In my view, GA Matlab toolbox is suitable for your work, because it is complete. But if you want another GA code, you can search the Matlab site for other code which is written by users. Others put some links above.

For the genetic algorithm processing, I recommend that you develop your own code by first modeling your populations (individual), select a type of crossover (number of points of crossing), taking parts of the parents vectors and merge them to generate the offspring. Once you are finished crossing all parents, add some random elements with a low percentage for mutation.

Also, if you prefer using predefined functions in Matlab, check this link:

https://www.mathworks.com/help/gads/genetic-algorithm-options.html

Dr. Kalami and his team have developed a perfect repository of the open access Matlab and Python source codes for a wide range of optimization methods.

https://yarpiz.com/

I'd have to agree with Mehdi Neshat. If a basic version of the GA algorithm is what anyone is searching for, then there is no better place to find it than Yarpiz. An explanation of the mathematical formulation for the GA principle is also provided along with the code.

https://www.youtube.com/watch?v=FuWPDQz6Oe0

Good Luck