Clustering of hyper-heuristic selections using the Smith-Waterman algorithm for offline learning
William B. Yates, Edward C. Keedwell · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
Selection hyper-heuristics are methods that are typically used to solve computationally hard optimisation problems (see [1]). A selection hyper-heuristic selects heuristics from a given set of low level heuristics, deciding which heuristic to apply at a given point during the optimisation process. The sequences of low level heuristic selections and objective function values that result from the application of a simple selection hyper-heuristic to the HyFlex problem set (see [3]) are used to construct an offline learning database. The intention is to select effective subsequences of heuristics from this database and use them as inputs to machine learning algorithms in order to improve optimisation.