Estimation Models Generation using Linear Genetic Programming

Authors

  • Javier Martínez Canillas
  • Roberto Sánchez
  • Benjamín Barán

DOI:

https://doi.org/10.19153/cleiej.12.3.4

Abstract

The use of decision rules and estimation techniques is increasingly common for decision mak-
ing. In recent years studies were conducted which applies Genetic Programming (GP) to obtain
rules to make predictions. A new branch in the area of Evolutionary Algorithms (EA) is Linear
Genetic Programming (LGP). LGP evolves instructions sequences of an imperative programming
language. This paper proposes estimation models generation for time series forecasting using LGP.
The forecasting result for the Consumer Price Index (CPI) and the price of soybeans per ton shows
the potential of this new proposal.

Downloads

Published

2009-12-01

Issue

Vol. 12 No. 3 (2009): Regular Issue and Special Issue of Best Papers presented at CLEI 2008, Santa Fe, Argentina

Section

Articles

License

CLEIej is supported by its home institution, CLEI, and by the contribution of the Latin American and international researchers community, and it does not apply any author charges whatsoever for submitting and publishing. Since its creation in 1998, all contents are made publicly accesibly. The current license being applied is a (CC)-BY license (effective October 2015; between 2011 and 2015 a (CC)-BY-NC license was used).