Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples

arXiv:2404.05359v1 Announce Type: cross
Abstract: The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajectories obtained from running a solver as input have shown promise. However, it is unclear to what extent these trajectories reliably discriminate between solvers. We propose a meta approach to generating discriminatory trajectories with respect to a portfolio of solvers. The algorithm-configuration tool irace is used to …

abstract algorithm arxiv cs.lg cs.ne data feature free however improving part performance prediction running samples solver success train training type via