An improved chromosome formulation for genetic algorithms applied to variable selection with the inclusion of interaction terms
This work addresses a bottleneck in variable selection for high-dimensional data analysis, but it is incremental as it modifies an existing chromosome formulation.
The paper tackles the scalability and sparsity issues in genetic algorithms for variable selection when including interaction terms in high-dimensional datasets, resulting in improved computational efficiency and sparsity compared to standard methods.
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.