Subset selection in multiple linear regression models: A hybrid of genetic and simulated annealing algorithms

The question of variable selection in a multiple linear regression model is a major open research topic in statistics. The subset selection problem in multiple linear regression deals with the selection of a minimal subset of input variables without loss of explanatory power. In this paper, we adapt the genetic and simulated annealing algorithms for variable selection in multiple linear regression. The performance of this hybrid heuristic method is compared to those obtained by forward selection, backward elimination and classical genetic algorithm search. A comparative analysis on the literature data sets and simulation data shows that our hybrid heuristic method may suggest efficient alternative to traditional subset selection methods for the variable selection problem in multiple linear regression models.