Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem

It is of great interest to build recommendation systems capable of choosing the best solver for a particular problem of a combinatorial optimisation task given past runs of solvers in various problems of that optimisation task. In this paper, a meta-learning approach is proposed to predict which metaheuristic is the best solver for MaxSAT problems. The proposal includes the creation of new meta-features derived from graph descriptions of MaxSAT problems and an interpretation of the meta-model. Our approach successfully selected the best metaheuristic to solve each problem in 87% of the cases. Also, the new meta-features have shown to be as good as the state-of-the-art meta-features, and the meta-model interpretation found interesting problem-specific knowledge.