Modified choice function heuristic selection for the multidimensional knapsack problem
Drake, John H. and Özcan, Ender and Burke, Edmund K. (2014) Modified choice function heuristic selection for the multidimensional knapsack problem. In: Genetic and evolutionary computing. Advances in Intelligent Systems and Computing (329). Springer, pp. 225-234. ISBN 9783319122854
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.
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