creat a genetic algorithm that with 3 input variables , nonlinear constraint function and objective function all in seperate scritps in matlab

main.m
function y = objectiveFunction(x)
% x is a vector with 3 values
    y = % calculate objective function value for x
end
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main.m
function [c, ceq, dc, dceq] = constraintFunction(x)
% x is a vector with 3 values
    c = % calculate nonlinear inequality constraints for x
    ceq = % calculate nonlinear equality constraints for x
    dc = % calculate gradients of c with respect to x
    dceq = % calculate gradients of ceq with respect to x
end
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main.m
options = optimoptions('ga');
options.ConstraintTolerance = 1e-6;
options.FunctionTolerance = 1e-6;
options.Display = 'final';

options.ConstraintNonlinearFcn = @constraintFunction;
options.ObjectiveFcn = @objectiveFunction;
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main.m
lb = % define lower bounds for x
ub = % define upper bounds for x
x0 = % define initial population of x

[x, fval, exitflag, output, population, scores] = ga(@objectiveFunction, 3, [], [], [], [], lb, ub, @constraintFunction, options);
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