Variable Selection in Multiple Linear Regression Using a Genetic Algorithm

View Sample PDF

Author(s): Javier Trejos (University of Costa Rica, Costa Rica), Mario A. Villalobos-Arias (University of Costa Rica, Costa Rica)and Jose Luis Espinoza (Technological Institute of Costa Rica, Costa Rica)
Copyright: 2016
Pages: 27
Source title: Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics
Source Author(s)/Editor(s): Pandian Vasant (University of Technology Petronas, Malaysia), Gerhard-Wilhelm Weber (Middle East Technical University, Turkey)and Vo Ngoc Dieu (Ho Chi Minh City University of Technology, Vietnam)
DOI: 10.4018/978-1-4666-9644-0.ch005

Keywords: Algorithms / Artificial Intelligence / Computer Science & IT / Information Science Reference

Purchase

View Variable Selection in Multiple Linear Regression Using a Genetic Algorithm on the publisher's website for pricing and purchasing information.

Abstract

In this article it is studied the application of a genetic algorithm in the problem of variable selection for multiple linear regression, minimizing the least squares criterion. The algorithm is based on a chromosomic representation of variables that are considered in the least squares model. A binary chromosome indicates the presence (1) or absence (0) of a variable in the model. The fitness function is based on the adjusted square R, proportional to the fitness for chromosome selection in a roulette wheel model selection. Usual genetic operators, such as crossover and mutation are implemented. Comparisons are performed with benchmark data sets, obtaining satisfying and promising results.

The IRMA Community

Research IRM

Variable Selection in Multiple Linear Regression Using a Genetic Algorithm

Purchase

Abstract

Related Content

IRMA Sponsors