Distributed Genetic Algorithm for Feature Selection

Michael Potter, Ayberk Yarkin Yildiz, Nishanth Marer Prabhu, Cameron Gordon

Abstract: 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).

External IDs:dblp:journals/corr/abs-2401-10846