Cost-Efficient Training for Automated Algorithm Selection

When solving decision and optimisation problems, many competing algorithms have complementary strengths. Typically, there is no single algorithm that works well for all instances of a problem. Automated algorithm selection has been shown to work very well for choosing a suitable algorithm for a given instance. However, the cost of training can be prohibitively large due to the need of running all candidate algorithms on a set of training instances. In this work, we explore reducing this cost by selecting specific instance/algorithm combinations to train on, rather than requiring all algorithms for all instances. We approach this problem in three ways: using active learning to decide based on prediction uncertainty, augmenting the algorithm predictors with a timeout predictor, and collecting training data using a progressively increasing timeout. We evaluate combinations of these approaches on six datasets from ASLib and present the reduction in labelling cost.