First of all, you should use regress for multiple linear regression. However, if you want to know whether it is possible to use ga for it, the answer is "yes".
First let's create a dataset for a multiple linear regression analysis in the form of 
.
.rng (0);
OriginalA = 2;
OriginalB1 = 1;
OriginalB2 = -4;
X1 = (0:10);
X2 = (5:12);
[DataX1, DataX2] = meshgrid (X1, X2);
DataY = OriginalA + OriginalB1*DataX1 + OriginalB2*DataX2 + 4*rand(numel(X2),numel(X1));
Define the objective function. Regression models minimize the mean squared error of the estimation (MSE).
function f = MSE (x, DataX1, DataX2, DataY)
a = x(1);
b1 = x(2);
b2 = x(3);
YHat = a + b1*DataX1 + b2*DataX2;
f = mean((DataY-YHat).^2, 'all');
Run the Genetic Algorithm to .
fun = @(x)MSE(x, DataX1, DataX2, DataY); % minimize MSE
nvars = 3;
A = [];
b = [];
Aeq = [];
beq = [];
lb = [-10,-10,-10];
ub = [10,10,10];
nonlcon = [];
options = optimoptions ('ga', 'MaxGenerations', 1e3);
[x,fval,exitflag,output] = ...
ga (fun,nvars,A,b,Aeq,beq,lb,ub,nonlcon,options);
Investigate the results.
RegressionA = x(1);
RegressionB1 = x(2);
RegressionB2 = x(3);
RegressionY = RegressionA + RegressionB1*DataX1 + RegressionB2*DataX2;
hold on
scatter3 (DataX1(:), DataX2(:), DataY(:), 18, ...
'Marker', 'o', ...
'MarkerEdgeColor', 'none', ...
'MarkerFaceColor', 'k', ...
'DisplayName', 'Original data');
surf (DataX1, DataX2, RegressionY, ...
'FaceAlpha', 0.8, ...
'EdgeColor', 'none', ...
'DisplayName', 'Regression model')
text (-3, 9, -28, ...
sprintf("MSE = %f\na = %f\nb_1=%f\nb_2= %f", fval, RegressionA, RegressionB1, RegressionB2));
hold off
xlabel ('x_1');
xlabel ('x_2');
xlabel ('y');
view ([-120,25]);
grid ('on')
box ('on');
legend ('Location', 'Northwest');
