John H. Drake
Modified choice function heuristic selection for the multidimensional knapsack problem
Drake, John H.; Özcan, Ender; Burke, Edmund K.
ENDER OZCAN firstname.lastname@example.org
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 computingSpringer. doi:10.1007/978-3-319-12286-1_23
|Acceptance Date||Oct 18, 2014|
|Publication Date||Oct 18, 2014|
|Deposit Date||Jun 13, 2016|
|Peer Reviewed||Peer Reviewed|
|Series Title||Advances in Intelligent Systems and Computing|
|Book Title||Genetic and evolutionary computing|
|Keywords||Hyper-heuristics, Choice Function, Heuristic Selection, Multidimensional Knapsack Problem, Combinatorial Optimization|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0|
This file is under embargo due to copyright reasons.
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