creat a genetic algorithm that with 3 input variables , nonlinear constraint function and objective function all in seperate scripts also creat seperate scripts for the variables in matlab

main.m
function f = objective(x)
% x: vector of decision variables
% f: objective function value

% Calculate the objective function value
% Here is an example objective function
f = 100*(x(2) - x(1)^2)^2 + (1 - x(1))^2;
end
218 chars
9 lines

main.m
function [c, ceq] = nonlincon(x)
% x: vector of decision variables
% c: inequality constraints
% ceq: equality constraints (empty for this example)

% Calculate the inequality constraint values
% Here is an example non-linear constraint function
c = [x(1)+x(2)-1.5;
     -x(1)+2*x(2)-1];
ceq = [];
end
302 chars
12 lines

main.m
% Define the genetic algorithm parameters
nvars = 3; % Number of decision variables
lb = [0, 0, 0]; % Lower bound of decision variables
ub = [1, 1, 1]; % Upper bound of decision variables
A = []; % No linear inequality constraints
b = []; 
Aeq = []; % No linear equality constraints
beq = [];
nonlcon = @nonlincon; % Non-linear constraint function
fitnessfcn = @objective; % Objective function

% Run the genetic algorithm
options = gaoptimset('Display', 'iter');
[x, fval] = ga(fitnessfcn, nvars, A, b, Aeq, beq, lb, ub, nonlcon, options);
disp(['Optimal point: x = [', num2str(x(1)), ', ', ...
    num2str(x(2)), ', ', num2str(x(3)), '], fval = ', num2str(fval)]);
667 chars
17 lines

main.m
% Define the value of decision variable 1
x1 = x(1); % This assumes the value of x has already been calculated in ga_main.m

% Use the value of decision variable 1 for some other purpose
% Here is an example
disp(['The value of decision variable 1 is: ', num2str(x1)]);
270 chars
7 lines