Markov chain hyper-heuristic (MCHH): an online selective hyper-heuristic for multi-objective continuous problems

Authors:
Kent McClymont;Edward C. Keedwell
Affiliations:
University of Exeter, Exeter, United Kingdom;University of Exeter, Exeter, United Kingdom
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

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Abstract

In this paper we present the Markov chain Hyper-heuristic (MCHH), a novel online selective hyper-heuristic which employs reinforcement learning and Markov chains to provide an adaptive heuristic selection method. Experiments are conducted to demonstrate the efficacy of the method and comparisons are made with standard heuristics, a random hyper-heuristic and a multi-objective hyper-heuristic from the literature. The approaches are compared on a small number of evaluations of the multi-objective DTLZ test problems to reflect the computational limitations of expensive optimisation problems. The results demonstrate the MCHH robust and reliable performance on these problems.