Definición de una red neuronal para clasificación por medio de un programa evolutivo
Abstract
Artificial neural nets called Multi-layer Perceptrons (MP) are an extremely useful tool for solving classification problems. It has been shown that MPs with a single hidden layer can satisfactorily separate the classes involved in a given problem: however, the number of hidden units required is unknown as there is no exact method for calculating them. The present work describes a way, using an evolutionary program (EP) with variable length chromosomes, of finding a suitable number of hidden units, as well as the weights on all the connections, thereby defining the architecture of a MP for a given classification problem. Only the number of input and output units are considered given. The operators used by the EP are the selection and two forms of mutation, and these are specified in the paper.
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