abstract
genetic algorithms are a well-known method for tackling the problem of variable selection. as
they are non-parametric and can use a large variety of fitness functions, they are well-suited as a
variable selection wrapper that can be applied to many different models. in almost all cases, the
chromosome formulation used in these genetic algorithms consists of a binary vector of length n
for n potential variables indicating the presence or absence of the corresponding variables.
while the aforementioned chromosome formulation has exhibited good performance for relatively
small n, there are potential problems when the size of n grows very large, especially when
interaction terms are considered. we introduce a modification to the standard chromosome
formulation that allows for better scalability and model sparsity when interaction terms are
included in the predictor search space. experimental results show that the indexed chromosome
formulation demonstrates improved computational efficiency and sparsity on high-dimensional
datasets with interaction terms compared to the standard chromosome formulation.
keywords : genetic algorithm, chromosome, variable selection, feature selection, interaction terms
, high dimensional data