We empirically show that process-based Parallelism speeds up the Genetic Algorithm (GA) for Feature Selection (FS) 2x to 25x, while additionally increasing the Machine Learning (ML) model performance on metrics such as F1-score, Accuracy, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC).
@article{arxiv.2401.10846,
title = {Distributed Genetic Algorithm for Feature Selection},
author = {Michael Potter and Ayberk Yarkın Yıldız and Nishanth Marer Prabhu and Cameron Gordon},
journal= {arXiv preprint arXiv:2401.10846},
year = {2024}
}