GA optimization

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.

Answer 1

[Seth DeLand]

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

[Abul Fujail]

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