Improving Algorithm Selection and Performance Prediction by Learning Discriminating Training Samples

The core message of this article is that tuning the parameters of a simple Simulated Annealing algorithm to generate discriminatory trajectories can improve the performance of machine learning models for algorithm selection and performance prediction, compared to using either raw trajectory data or exploratory landscape features.