Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)

A Genetic Algorithm-Based Quasi-Linear Regression Method and Application

Authors
Fachao Li, Kena Zhang
Corresponding Author
Fachao Li
Available Online February 2013.
DOI
10.2991/isccca.2013.108How to use a DOI?
Keywords
quasi-linear function, regression analysis, genetic algorithm, prediction
Abstract

Regression analysis, as an important branch of statistics, is an effective tool for scientific prediction. Genetic algorithm is an optimization search algorithm in computational mathematics. In this paper, a new regression model named quasi-linear regression model is established. Further, its implementation method is introduced in detail. Then by taking the population development of Hebei province as an example, we conduct the fitting problem and short-term prediction. Moreover, we compare the fitting effect and the prediction results of two models.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)
Series
Advances in Intelligent Systems Research
Publication Date
February 2013
ISBN
10.2991/isccca.2013.108
ISSN
1951-6851
DOI
10.2991/isccca.2013.108How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fachao Li
AU  - Kena Zhang
PY  - 2013/02
DA  - 2013/02
TI  - A Genetic Algorithm-Based Quasi-Linear Regression Method and Application
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)
PB  - Atlantis Press
SP  - 438
EP  - 441
SN  - 1951-6851
UR  - https://doi.org/10.2991/isccca.2013.108
DO  - 10.2991/isccca.2013.108
ID  - Li2013/02
ER  -