Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches
This work addresses the problem of optimizing algorithm performance across diverse instances for researchers and practitioners in computational optimization, offering a significant improvement over standard methods.
The paper tackles the algorithm selection problem by proposing integer programming-based approaches to build decision trees that automate feature selection, problem grouping, and algorithm configuration, resulting in a 40% average reduction in total running time compared to a 22% reduction from selecting a single best setting.
Even though it is well known that for most relevant computational problems different algorithms may perform better on different classes of problem instances, most researchers still focus on determining a single best algorithmic configuration based on aggregate results such as the average. In this paper, we propose Integer Programming based approaches to build decision trees for the Algorithm Selection Problem. These techniques allow automate three crucial decisions: (i) discerning the most important problem features to determine problem classes; (ii) grouping the problems into classes and (iii) select the best algorithm configuration for each class. To evaluate this new approach, extensive computational experiments were executed using the linear programming algorithms implemented in the COIN-OR Branch & Cut solver across a comprehensive set of instances, including all MIPLIB benchmark instances. The results exceeded our expectations. While selecting the single best parameter setting across all instances decreased the total running time by 22%, our approach decreased the total running time by 40% on average across 10-fold cross validation experiments. These results indicate that our method generalizes quite well and does not overfit.