Nonlinear Modeling: Genetic Programming vs. Fast Evolutionary Programming

Authors

Minglei Duan, Marquette University
Richard J. Povinelli, Marquette UniversityFollow

Document Type

Conference Proceeding

Language

eng

Publication Date

2001

Publisher

The American Society of Mechanical Engineers

Source Publication

Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Data Mining and Complex systems: Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2001)

Source ISSN

0791801764

Abstract

Both Genetic Programming (GP) and Fast Evolutionary Programming (FEP) combined with a Reduced Parameter Bilinear (RPBL) model have been recognized as effective time series modeling methods. This study compares the performance of these two methods for their ability to model time series data in terms of their accuracy and time efficiency. A brief review of GP and FEP are presented. Then the accuracy and time efficiency of these two methods are evaluated on several different time series. The performances of the two methods are compared against each other.

Comments

Published as part of the proceedings of The Artificial Neural Networks in Engineering Conference (ANNIE 2001): 171-176. Publisher link.

Recommended Citation

Duan, Minglei and Povinelli, Richard J., "Nonlinear Modeling: Genetic Programming vs. Fast Evolutionary Programming" (2001). Electrical and Computer Engineering Faculty Research and Publications. 270.
https://epublications.marquette.edu/electric_fac/270