Regression modeling based on improved genetic algorithm
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 pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2017-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/257830 |
| _version_ | 1827283071577096192 |
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| author | Shi Minghua Xiao Qingxian Zhou Benda Yang Feng |
| author_facet | Shi Minghua Xiao Qingxian Zhou Benda Yang Feng |
| author_sort | Shi Minghua |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:29:39Z |
| format | Article |
| id | doaj.art-2507de4bd3d143559162889dcd21296c |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:29:39Z |
| publishDate | 2017-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-2507de4bd3d143559162889dcd21296c2024-04-15T14:01:23ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392017-01-01241637010.17559/TV-20160525104127Regression modeling based on improved genetic algorithmShi Minghua0Xiao Qingxian1Zhou Benda2Yang Feng3Business school, University of Shanghai for Science and Technology, No. 334 Jungong Road, 200093, Shanghai, ChinaBusiness school, University of Shanghai for Science and Technology, No. 334 Jungong Road, 200093, Shanghai, ChinaCollege of Finance and Mathematics & Financial Risk Intelligent Control and Prevention Institute, West Anhui University, No. 1 Yunluqiao West Road, 237012, Lu’an, ChinaBusiness school, University of Shanghai for Science and Technology, No. 334 Jungong Road, 200093, Shanghai, ChinaRegression 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.https://hrcak.srce.hr/file/257830genetic algorithmLatin hypercube samplingregression analysisregression model selection |
| spellingShingle | Shi Minghua Xiao Qingxian Zhou Benda Yang Feng Regression modeling based on improved genetic algorithm Tehnički Vjesnik genetic algorithm Latin hypercube sampling regression analysis regression model selection |
| title | Regression modeling based on improved genetic algorithm |
| title_full | Regression modeling based on improved genetic algorithm |
| title_fullStr | Regression modeling based on improved genetic algorithm |
| title_full_unstemmed | Regression modeling based on improved genetic algorithm |
| title_short | Regression modeling based on improved genetic algorithm |
| title_sort | regression modeling based on improved genetic algorithm |
| topic | genetic algorithm Latin hypercube sampling regression analysis regression model selection |
| url | https://hrcak.srce.hr/file/257830 |
| work_keys_str_mv | AT shiminghua regressionmodelingbasedonimprovedgeneticalgorithm AT xiaoqingxian regressionmodelingbasedonimprovedgeneticalgorithm AT zhoubenda regressionmodelingbasedonimprovedgeneticalgorithm AT yangfeng regressionmodelingbasedonimprovedgeneticalgorithm |