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Variable selection is a challenged procedure when there is the large number of explanatory variables and interaction effects are expected in the model. The number of possible models can be very large so that the stepwise algorithm tends to give a local optimal model. This paper aims to apply the genetic algorithm with 6 types of crossover operators for 4 real datasets and simulated data. Both linear regression and binomial logistic regression are of interest and the Akaike’s information criterion (AIC) is used as a criterion for variable selections. For simulated data, the explanatory variables are set to have no correlation and to have correlations with the First-order autoregressive structure in which correlations equal 0.3, 0.5, and 0.8. The results will be compared with those from stepwise variable selections: forward selection, backward elimination, and alternating stepwise selection. Furthermore, we have proposed a new criterion representing percentage of independent variables correctly included into a model. The results show that compared to the stepwise variable selection, the genetic algorithm can find the model with a lower AIC. If comparing the 6 crossover operators, we find that the (m-1)-point crossover will choose the less suitable model and this statistically differs from other crossover operators. And, the shuffle crossover and uniform crossover are not statistically significant in all cases in the study.
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