  Feature selection plays a pivotal role in the data preprocessing and
model-building pipeline, significantly enhancing model performance,
interpretability, and resource efficiency across diverse domains. In
population-based optimization methods, the generation of diverse individuals
holds utmost importance for adequately exploring the problem landscape,
particularly in highly multi-modal multi-objective optimization problems. Our
study reveals that, in line with findings from several prior research papers,
commonly employed crossover and mutation operations lack the capability to
generate high-quality diverse individuals and tend to become confined to
limited areas around various local optima. This paper introduces an
augmentation to the diversity of the population in the well-established
multi-objective scheme of the genetic algorithm, NSGA-II. This enhancement is
achieved through two key components: the genuine initialization method and the
substitution of the worst individuals with new randomly generated individuals
as a re-initialization approach in each generation. The proposed
multi-objective feature selection method undergoes testing on twelve real-world
classification problems, with the number of features ranging from 2,400 to
nearly 50,000. The results demonstrate that replacing the last front of the
population with an equivalent number of new random individuals generated using
the genuine initialization method and featuring a limited number of features
substantially improves the population's quality and, consequently, enhances the
performance of the multi-objective algorithm.
