Analysing Heuristic Subsequences for Offline Hyper-heuristic Learning

A selection hyper-heuristic is used to optimise a number of well-known benchmark problems. The resulting sequences of heuristics and objective function values are used to create a database. The sequences in the database are broken down into subsequences and the concept of a logarithmic return is used to discriminate between "effective" subsequences, which tend to decrease the objective value, and "disruptive" subsequences, which tend to increase the objective value. These subsequences are then employed in a sequenced based hyper-heuristic and evaluated on unseen benchmark problems. The results demonstrate that the "effective" subsequences perform better than the "disruptive" subsequences across a number of problem domains with 99% confidence. The identification of subsequences of heuristic that can be shown to be effective across a number of problems or problem domains could have important implications for the design of hyper-heuristics.