Learning a hidden Markov model-based hyper-heuristic

A simple model shows how a reasonable update scheme for the probability vector by which a hyper-heuristic chooses the next heuristic leads to neglecting useful mutation heuristics. Empirical evidence supports this on the MaxSat, TravelingSalesman, PermutationFlowshop and VehicleRoutingProblem problems. A new approach to hyper-heuristics is proposed that addresses this problem by modeling and learning hyper-heuristics by means of a hidden Markov Model. Experiments show that this is a feasible and promising approach.

ISBN:978-3-319-19083-9

Publication year:2015

BOF-keylabel:yes

IOF-keylabel:yes

Authors from:Higher Education

Accessibility:Open