Cart (Loading....) | Create Account
Close category search window

Evolution of functional link networks

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
$31 $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

3 Author(s)
Sierra, A. ; Escuela Tecnica Superior de Inf., Univ. Autonoma de Madrid, Spain ; Macias, J.A. ; Corbacho, F.

This paper addresses the genetic design of functional link networks (FLN). FLN are high-order perceptrons (HOP) without hidden units. Despite their linear nature, FLN can capture nonlinear input-output relationships, provided that they are fed with an adequate set of polynomial inputs, which are constructed out of the original input attributes. Given this set, it turns out to be very simple to train the network, as compared with a multilayer perceptron (MLP). However finding the optimal subset of units is a difficult problem because of its nongradient nature and the large number of available units, especially for high degrees. Some constructive growing methods have been proposed to address this issue, Here, we rely on the global search capabilities of a genetic algorithm to scan the space of subsets of polynomial units, which is plagued by a host of local minima. By contrast, the quadratic error function of each individual FLN has only one minimum, which makes fitness evaluation practically noiseless. We find that surprisingly simple FLN compare favorably with other more complex architectures derived by means of constructive and evolutionary algorithms on some UCI benchmark data sets. Moreover, our models are especially amenable to interpretation, due to an incremental approach that penalizes complex architectures and starts with a pool of single-attribute FLN

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:5 ,  Issue: 1 )

Date of Publication:

Feb 2001

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.