Learning a Hidden Markov Model-Based Hyper-heuristic | Zendy

Willem Van Onsem | Zendy; Bart Demoen | Zendy; Patrick De Causmaecker | Zendy

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Learning a Hidden Markov Model-Based Hyper-heuristic

Author(s) -

Willem Van Onsem,

Bart Demoen,

Patrick De Causmaecker

Publication year - 2015

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

DOI - 10.1007/978-3-319-19084-6_7

Subject(s) - heuristics , heuristic , computer science , artificial intelligence , simple (philosophy) , markov chain , machine learning , markov model , scheme (mathematics) , mathematical optimization , mathematics , mathematical analysis , philosophy , epistemology

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.

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