Improving the performance of vector hyper-heuristics through local search

Abstract

Hyper-heuristics enable us to selectively apply the most suit-able low-level heuristic depending on the properties of the problem at hand. They can be used for solving Constraint Satisfaction Problems (CSP) in dierent ways considering the variety of hyper-heuristics and low-level heuristics. A particular approach which has been receiving attention in the recent years is based on variable ordering using hyper-heuristics. A hyper-heuristic decides the next variable to process using a set of predened heuristics considering the features that describe the instance at a given point dur-ing the search in this framework. This study explores an approach in which each hyper-heuristic is represented as a set of vectors mapping instance features to heuristics for variable ordering. The results suggest that the proposed ap-proach is able to combine the strengths of dierent heuristics and compensate for their weaknesses performing better than each heuristic in isolation across a range of instances