Computational Intelligence and Neuroscience / 2016 / Article / Alg 1

Research Article

Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem

Algorithm 1

TrainDBN: training DBN to generate the PFKB.
Input: , , , (, are training data; , are testing data)
Output: PFKB
() Initial(DL, opts) // initial the structure of DL and the parameters opts
() DL = DLsetup(DBN, , opts) // layer-wise pre-training DL
() DL = DLtrain(DBN, , opts) // build up each layer of DL to train
() (DBN, opts) // after training each layer, passing the parameters to nn
() PFKB = train(, , , opts) // fine-tune the whole deep architecture
() accuracy = test(PFKB, , ) // accuracy is the criterion of the quality of PFKM, if it is too
small, then re-training after adjusting the model architecture or parameters
() return PFKB

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