Improving Algorithm-Selection and Performance-Prediction via Learning
  Discriminating Training Samples
Quentin Renau Emma Hart
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 tune the parameters of a simple Simulated Annealing algorithm (SA)
to produce trajectories that maximise the performance metrics of ML models
trained on this data. We show that when the trajectories obtained from the
tuned SA algorithm are used in ML models for algorithm-selection and
performance prediction, we obtain significantly improved performance metrics
compared to models trained both on raw trajectory data and on exploratory
landscape features.