A genetic programming hyper-heuristic for the multidimensional knapsack problem

Drake, John H. and Hyde, Matthew and Khaled, Ibrahim and Özcan, Ender (2014) A genetic programming hyper-heuristic for the multidimensional knapsack problem. Kybernetes, 43 (9/10). pp. 1500-1511. ISSN 0368-492X

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Abstract

Purpose: Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. Design/methodology/approach: Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings: The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value: In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort. © Emerald Group Publishing Limited.

Item Type: Article
Keywords: Artificial intelligence, genetic programming, heuristic generation, hyper-heuristics, multidimensional knapsack problem
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1108/K-09-2013-0201
Depositing User: Ozcan, Dr Ender
Date Deposited: 10 Mar 2016 11:46
Last Modified: 13 Sep 2016 21:36
URI: http://eprints.nottingham.ac.uk/id/eprint/32174

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