  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.
