Original scientific paper

https://doi.org/10.17559/TV-20160525104127

Regression modeling based on improved genetic algorithm

Shi Minghua ; Business school, University of Shanghai for Science and Technology, No. 334 Jungong Road, 200093, Shanghai, China
Xiao Qingxian ; Business school, University of Shanghai for Science and Technology, No. 334 Jungong Road, 200093, Shanghai, China
Zhou Benda ; College of Finance and Mathematics & Financial Risk Intelligent Control and Prevention Institute, West Anhui University, No. 1 Yunluqiao West Road, 237012, Lu’an, China
Yang Feng ; Business school, University of Shanghai for Science and Technology, No. 334 Jungong Road, 200093, Shanghai, China

Abstract

Regression model is a well-established method in data analysis with applications in various fields. The selection of independent variables and mathematically transformed in a regression model is often a challenging problem. Recently, some scholars have used evolutionary computation to solve this problem, but the result is not effective as we desired. The crossover operation in GA is redesigned by using Latin hypercube sampling, then combining two commonly used statistical criteria (AIC, BIC) we are presenting an improved genetic algorithm based for solving statistical model selection problem. The proposed algorithm can overcome strong path-dependence and rely on experience of classical approaches. Comparison of simulation results in solving statistical model selection problem with this improved GA, traditional genetic algorithm and classical algorithm for model selection show that the new GA has superiority in solution of quality, convergence rate and other various indices.

Keywords

genetic algorithm; Latin hypercube sampling; regression analysis; regression model selection

Hrčak ID:

174701

URI

https://hrcak.srce.hr/174701

Publication date:

10.2.2017.

Article data in other languages: croatian

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