Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming

We present SLUG, a recent method that uses genetic algorithms as a wrapper for genetic programming and performs feature selection while inducing models. SLUG was shown to be successful on different types of classification tasks, achieving state-of-the-art results on the synthetic datasets produced by GAMETES, a tool for embedding epistatic gene–gene interactions into noisy datasets. SLUG has also been studied and modified to demonstrate that its two elements, wrapper and learner, are the right combination that grants it success. We report these results and test SLUG on an additional six GAMETES datasets of increased difficulty, for a total of four regular and 16 epistatic datasets. Despite its slowness, SLUG achieves the best results and solves all but the most difficult classification tasks. We perform further explorations of its inner dynamics and discover how to improve the feature selection by enriching the communication between wrapper and learner, thus taking the first step toward a new and more powerful SLUG.