How GA can be hybridized with Neural network (with reference to Matlab).
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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.
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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