By Topic

G-Prop-II: global optimization of multilayer perceptrons using GAs

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
P. A. Castillo ; Dept. of Comput. Sci., Madrid Univ., Spain ; V. Rivas ; J. J. Merelo ; J. Gonzalez
more authors

A general problem in model selection is to obtain the right parameters that make a model fit observed data. For a multilayer perceptron (MLP) trained with backpropagation (BP), this means finding the right hidden layer size, appropriate initial weights and learning parameters. The paper proposes a method (G-Prop-II) 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 the learning rate of the network, and changes the number of neurons in the hidden layer through the application of specific genetic operators. G-Prop-II 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-II algorithm to several real world and benchmark problems shows that MLPs evolved using G-Prop-II 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

Published in:

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on  (Volume:3 )

Date of Conference: