Text-Based Feature-Free Automatic Algorithm Selection

Amanda Salinas-Pinto, Bryan Alvarado-Ulloa, Dorit S. Hochbaum, Matías Francia-Carramiñana, Ricardo Ñanculef, Roberto Javier Asín Achá

Abstract: Automatic Algorithm Selection involves predicting which solver, among a portfolio, will perform best for a given problem instance. Traditionally, the design of algorithm selectors has relied on domain-specific features crafted by experts. However, an alternative approach involves designing selectors that do not depend on domain-specific features, but receive a raw representation of the problem’s instances and automatically learn the characteristics of that particular problem using Deep Learning techniques. Previously, such raw representation was a fixed-sized image, generated from the input text file specifying the instance, which was fed to a Convolutional Neural Network. Here we show that a better approach is to use text-based Deep Learning models that are fed directly with the input text files specifying the instances. Our approach improves on the image-based feature-free models by a significant margin and furthermore matches traditional Machine Learning models based on basic doma