A genetic algorithm approach to find the best regression/econometric model among the candidates

摘要

Although statistical modeling is a common task in different fields of science, it is still difficult to estimate the best model that can accurately describe inherent characteristics of a system for which historical or experimental data are available. Since we may classify estimating techniques as optimizations, we can model this problem as an optimization problem and solve it by a new heuristic algorithm like neural networks, genetic algorithms, and tabu search or by classic ones such as regression and econometric models.In this paper, we propose a new type of genetic algorithm to find the best regression model among all suggested and evaluate its performances by an economical case study.