genetic algorithm to optimize the variable of linear regression(a,b1,b2)

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I need some codes for optimize my prediction(using linear regresion) in MATLAB. I am new to genetic algorithm so if anyone has a code that can do this that would help me start off will be greatly appreciated.

Antworten (2)

Abdolkarim Mohammadi
Abdolkarim Mohammadi am 20 Jul. 2020
Bearbeitet: Abdolkarim Mohammadi am 21 Jul. 2020
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');

Alan Weiss
Alan Weiss am 20 Jul. 2020
Bearbeitet: Alan Weiss am 20 Jul. 2020
While you CAN solve a linear regression using ga, it is unwise to do so. Your answers will be faster and more accurate while using less memory by using the MATLAB mldivide function (\). For an example, see Overdetermined Systems.
Alan Weiss
MATLAB mathematical toolbox documentation

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