When the genetic algorithm has integer constraint issues, how to customize the variogram to eliminate the ignored warning

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Commented: Alan Weiss on 2 May 2023
Use the genetic algorithm to solve the single target minimum and the optimal solution of three variables, where two variables are real numbers and one variable is an integer, in order to improve the genetic algorithm, the taboo search algorithm is used to make the custom variogram of the mutation operator, but the runtime appears, warning: Problem has integer constraints. The following options will be ignored by GA:MutationFcn.Therefore, many different functions still cannot solve this warning.
How to solve this mixed integer problem? If I don't define integer intcon in options, do I need to redefine selection, crossing, variogram? Or do you only need to constrain the variable integer in the variogram

Alan Weiss on 10 Apr 2023
I am not sure what you mean by "variogram." But I think that I know the reason for your difficulty. As the Release Notes state, until R2021b the genetic algorithm did not allow custom mutation or crossover functions in integer-constrained problems. If you want to use custom mutation or crossover functions, upgrade to R2021b or later.
Alan Weiss
MATLAB mathematical toolbox documentation
Jungeon Kim on 2 May 2023
I have a difficulty too for the same problem. My optimization problem has 'integer design variable' and 'linear/nonlinear inequality constraint'. I have to modify the crossover and mutation function referring to the default two operation functions, i.e., crossoverlaplace and mutationpower.
Then, can I open the function files 'crossoverlaplace' and 'mutationpower' exist after R2021b?
Alan Weiss on 2 May 2023
As the Release Notes for R2021b state, you can (but do not have to) give alternate crossover and mutation functions for the genetic algorithm. Because you have integer variables, I suggest that you use the mutationgaussian mutation function.
Alan Weiss
MATLAB mathematical toolbox documentation

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