A new algorithm for solving economic inverse problems based on gene expression programming

作者：Li Kangshun^*; Liu Yueyong; Luo Jing; Wang Dan

来源：International Journal of Advancements in Computing Technology, 2012, 4(21): 246-253.

DOI：10.4156/ijact.vol4.issue21.29

• Evolution algorithms
• GDP
• Gene expression programming
• Impact factor
• Inverse models
• Regression
• Regression algorithms

Gene Expression Programming (GEP) is a new evolution algorithm based on genotype and phenotype, and complex non-liner problems can be transformed to simple liner codes by using GEP. In this paper, a new algorithm for solving economic inverse problems based on GEP (EIPGEP Algorithm) is presented, and compared with the traditional economic regression algorithms, much more accurate solutions could be obtained in solving inverse problem models built by using this algorithm. Especially, when there are multiple GDP impact factors existing in solving the economic inverse problems model, it is more superior to solve the problems by using this new algorithm than by using the traditional regression algorithms. Experiments show that the fitted data and the forecasting results solved by using this new algorithm to build the economic inverse problem models of GDP with multiple GDP impact factors are much more accurate, compared with the traditional economic regression inverse models.

• 出版日期2012
• 单位华南农业大学