Title: Learning a hidden Markov model-based hyper-heuristic
Authors: Van Onsem, Willem ×
Demoen, Bart
De Causmaecker, Patrick #
Issue Date: 12-Jan-2015
Publisher: Springer
Host Document: Learning and Intelligent Optimization - 9th International Conference, LION 2015. Revised Selected Papers vol:8994 pages:74-88
Series Title: Lecture Notes in Computer Science
Conference: Learning and Intelligent OptimizatioN Conference edition:9 location:Lille, France date:12-15 january 2015
Abstract: 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.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
Computer Science, Campus Kulak Kortrijk
× corresponding author
# (joint) last author

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