Hi everyone,
I am developing an agent model based on the CRNN (continuous time recurrent
neural networks, implemented by J.J.Merello in Neurolib-2.1) the actual model
runs fine.
But as mentioned for some authors, the best way to improve their behaviour
(changing weights and bias terms) is through a Genetic Algorithm (there are
also one GA, Breeder-2.1 developed by JJMerello) but I don´t know how to
implement it. I have read the respective documentation, witch is clear, but
still some very basic things are not clear to me, because I have no clue on GA.
As far as I understood, the model to be optimised, in this case the NN, should
be created by the GA. As this model is fixed, (I don´t want to optimised the
architecture, it is full connection, nor the number of neurons) I suppose
the GA should create just one copy of the model. Which in turn should be
utilized by the fitness function to evaluate the entire population of
chromosomes (which represent the weights to be optimised). If this one copy
supposition is right, still is not clear to me, if the model should be
reinitialised every time for the new set of weights, or a new model as to be
created.
On the other hand, is all this is right, I suppose I have to run the model in
batch mode, but it is the most important part for me, not the GA. So how can
I visualize the result?
I hope some one can give me some directions,
Thanks,
David Camacho
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