GA optimization
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I have developed an neural network with 4 input nodes, 4 hidden nodes and one output node using feed forward back propagation network. Now I need to use GA to optimize and determine the maximum output. How to define the ANN model as the fitness function.
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Seth DeLand
2012-2-28
You can create a function handle to the network and then pass that function handle into GA. Note that GA will pass in a row vector, so if your NN accepts a column vector as input it will need to be transposed. Also, GA minimizes the objective function so we add the negative sign to flip it from a maximization problem to a minimization problem. For example:
objFcn = @(x) -sim(net,x'); % Function that simulates NN and returns output
[xOpt,fVal] = ga(objFcn, 4); % Find the minimum of objFcn with 4 inputs
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Abul Fujail
2012-4-4
Thank you very much...
I am also solving the same problem, Neural network with 4 input and want to optimize the weights to get the best result. My codes ase shown below in='input_train.tra';
p=load(in);
p=transpose(p);
tic;
net=feedforward([.1 .9;.1 .9;.1 .9;.1 .9],[7,1], {'logsig','logsig'},'trainlm');
net=init(net);
tr='target_train.tra';
x=load(tr);
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);
objFcn = @(x) -sim(net,x') % Function that simulates NN and returns output
[xOpt,fVal] = ga(objFcn, 4) % Find the minimum of objFcn with 4 inputs
The target vector consists of a vector with 80 values, and input consists of 4 vectors with 80 values in each vector... Now i want that the result should also consists of 80 values, one result for each input pattern... what changes should be done in the program... please suggest me... thank you.. Fujail
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