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Modified choice function heuristic selection for the Multidimensional Knapsack Problem


John H. Drake

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Professor of Computer Science and Operational Research

Edmund K. Burke


Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisation problems, including the multidimensional 0-1 knapsack problem (MKP). The traditional framework for iterative selection hyper-heuristics relies on two key components, a heuristic selection method and a move acceptance criterion. Existing work has shown that a hyper-heuristic using Modified Choice Function heuristic selection can be effective at solving problems in multiple problem domains. Late Acceptance Strategy is a hill climbing metaheuristic strategy often used as a move acceptance criteria in selection hyper-heuristics. This work compares a Modified Choice Function - Late Acceptance Strategy hyper-heuristic to an existing selection hyper-heuristic method from the literature which has previously performed well on standard MKP benchmarks.


Drake, J. H., Özcan, E., & Burke, E. K. (2014). Modified choice function heuristic selection for the Multidimensional Knapsack Problem. In Genetic and evolutionary computing (225–234).

Conference Name Eighth International Conference on Genetic and Evolutionary Computing
Conference Location Nanchang, China
Start Date Oct 18, 2014
End Date Oct 20, 2014
Acceptance Date Oct 18, 2014
Online Publication Date Sep 30, 2014
Publication Date Oct 27, 2014
Deposit Date Jun 13, 2016
Electronic ISSN 2194-5357
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Issue 329
Pages 225–234
Series Title Advances in Intelligent Systems and Computing
Series Number 329
Series ISSN 2194-5365
Book Title Genetic and evolutionary computing
ISBN 9783319122854
Keywords Hyper-heuristics, Choice Function, Heuristic Selection, Multidimensional Knapsack Problem, Combinatorial Optimization
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