Late acceptance-based selection hyper-heuristics for cross-domain heuristic search

Hyper-heuristics are high-level search methodologies used to find solutions to difficult real-world optimisation problems. Hyper-heuristics differ from many traditional optimisation techniques as they operate on a search space of low-level heuristics, rather than directly on a search space of potential solutions. A traditional iterative selection hyper-heuristic relies on two core components, a method for selecting a heuristic to apply at a given point and a method to decide whether or not to accept the result of the heuristic application. Raising the level of generality at which search methods operate is a key goal in hyper-heuristic research. Many existing selection hyper-heuristics make use of complex acceptance criteria which require problem specific expertise in controlling the various parameters. Such hyper-heuristics are often not general enough to be successful in a variety of problem domains. Late Acceptance is a simple yet powerful local search method which has only a single parameter to control. The contributions of this paper are twofold. Firstly, we will test the effect of the set of low-level heuristics on the performance of a simple stochastic selection mechanism within a Late Acceptance hyper-heuristic framework. Secondly, we will introduce a new class of heuristic selection methods based on roulette wheel selection and combine them with Late Acceptance acceptance criteria. The performance of these hyper-heuristics will be compared to a number of methods from the literature over six benchmark problem domains.