# GA-Neural Network Hybridization

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Abul Fujail on 1 Feb 2012
Commented: Greg Heath on 30 Jan 2017
How GA can be hybridized with Neural network (with reference to Matlab).
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Abul Fujail on 4 Apr 2012
in='input_train.tra';
p=transpose(p);
net=newff([.1 .9;.1 .9;.1 .9;.1 .9],[7,1], {'logsig','logsig'},'trainlm');
net=init(net);
tr='target_train.tra';
x=transpose(x);
net.trainParam.epochs=600;
net.trainParam.show=10;
net.trainParam.lr=0.3;
net.trainParam.mc=0.6;
net.trainParam.goal=0;
[net,tr]=train(net,p,x);
y=sim(net,p);
Some codes are shown above... i have 4 input vector and 1 target vector... i want to get the optimum weight with GA so that the mean square error between target and neural network predicted result is minimum. Please suggest me how the GA can be added with this neural network code..
thomas lass on 24 Dec 2016
I need the full codes of GA can be hybridized with Neural network

Greg Heath on 3 Feb 2012
I don't see how they can be combined to an advantage.
Just write the I/O relationship for the net in terms of input, weights and output: y = f(W,x). Then use the Global Optimization toolox to minimize the mean square error MSE = mean(e(:).^2) where e is the training error, e = (t-y) and t is the training goal.
Hope this helps.
Greg
##### 3 CommentsShow 1 older commentHide 1 older comment
Shipra Kumar on 30 Jan 2017
Edited: Shipra Kumar on 30 Jan 2017
greg how can u write y as a function. i am having similar difficulty while implementing ga-nn. would be glad if u could help
Greg Heath on 30 Jan 2017
y = B2+ LW*tansig( B1 + IW *x);

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