Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering / 608
Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instance to be solved. Algorithm portfolios aim to combine the strengths of multiple algorithmic approaches by training a classifier that selects or schedules solvers dependent on the given instance. We devise a new classifier that selects solvers based on a cost-sensitive hierarchical clustering model. Experimental results on SAT and MaxSAT show that the new method outperforms the most effective portfolio builders to date.