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Originally suggested by Koza and Rice (1991), GP neural networks combine the functional optimization methodology of GP but within stricter bounds than normal. Standard GP, without restriction, can evolve any set of operators and terminals that satisfy the problem, although often restrictions are placed on the total size of the tree that can be created (to restrict bloat). However, GPNN restricts the type of structure which can be evolved, rather than the size, so that the nal tree resembles a standard neural network. The operators are determined as a weighting function and an activation function. In addition to this, the terminal set is de ned as a set of oating-point values (for use in the operator functions) and the input variables gained from the database. The restrictions placed on the GP ensure that the root node always represents the output of the neural network, and that an activation function and weighting functions must directly descend from the root node. Beyond this, however, the algorithm is free to select from the operator and terminal set. This allows the GP element to optimize a neural network structure that minimizes the difference between output and desired response. There is no requirement for the network to use backpropagation or any other gradient descent learning method to determine the weights in the network, as these are determined during the optimization process. convert tiff to pdf c# itextsharp Convert Tiff file into PDF file using iTextSharp DLL | Anil Rathod
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