G-Prop: Global optimization of multilayer perceptrons using GAs

A general problem in model selection is to obtain the right parameters that make a model "t observed data. For a multilayer perceptron (MLP) trained with back-propagation (BP), this means "nding appropiate layer size and initial weights. This paper proposes a method (G-Prop, genetic backpropagation) that attempts to solve that problem by combining a genetic algorithm (GA) and BP to train MLPs with a single hidden layer. The GA selects the initial weights and changes the number of neurons in the hidden layer through the application of speci"c genetic operators. G-Prop 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 algorithm to several real-world and benchmark problems shows that MLPs evolved using G-Prop are smaller and achieve a higher level of generalization than other perceptron training algorithms, such as Quick-Propagation or RPROP, and other evolut...

• Type: Journal
• Year: 2000
• Where: IJON
• Authors: Pedro A. Castillo Valdivieso, Juan J. Merelo Guervós, Alberto Prieto, Víctor Manuel Rivas Santos, Gustavo Romero
• Keywords: Algorithms, Hidden Layer, IJON 2000, Initial Weights