Selecting Algorithms for the Quadratic Assignment Problem with a Multi-label Meta-Learning Approach
Augusto Dantas, Aurora Pozo · 2018
Meta-heuristic algorithms have been used to obtain feasible solutions in reasonable time for many NP-hard search problems. However, the performance of the algorithms heavily depends on the features of the problem. So, techniques for automatically choosing or designing algorithms have received attention from researchers in the past years. In this research, we investigate automated algorithm selection for the Quadratic Assignment Problem using a multi-label meta-learning approach. In multi-label learning, an instance can have more than one label at the same time, which is an adequate configuration for this scenario, since there can be ties in the performances of the algorithms. Two approaches for dealing with multi-label learning are compared, regarding both the prediction of which algorithms are the best and the resulting costs of the solutions. The experiments show that the algorithm selection process can yield better results than if we choose only one single algorithm to solve all instances.