G - Prop - III : Global Optimization of Multilayer Perceptrons using anEvolutionary
نویسندگان
چکیده
This paper proposes a new version of a method (G-Prop-III, genetic backpropaga-tion) that attempts to solve the problem of nding appropriate initial weights and learning parameters for a single hidden layer Mul-tilayer Perceptron (MLP) by combining a genetic algorithm (GA) and backpropagation (BP). The GA selects the initial weights and the learning rate of the network, and changes the number of neurons in the hidden layer through the application of speciic genetic operators. Besides, this new version of the algorithm includes BP training as a mutation operator. G-Prop-III combines the advantages of the global search performed by the GA over the MLP parameter space and the local search of the BP algorithm. The application of the G-Prop-III algorithm to several real-world and benchmark problems shows that MLPs evolved using G-Prop-III are smaller and achieve a higher level of generalization than other perceptron training algorithms, such as QuickPropagation or RPROP, and other evolutive algorithms, such as G-LVQ. It also shows some improvement over previous versions of the algorithm.
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