@article { , title = {A genetic programming hyper-heuristic for the multidimensional knapsack problem}, 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.}, doi = {10.1108/K-09-2013-0201}, eissn = {0368-492X}, issn = {0368-492X}, issue = {9/10}, journal = {Kybernetes}, publicationstatus = {Published}, publisher = {Emerald}, url = {https://nottingham-repository.worktribe.com/output/725255}, volume = {43}, keyword = {Artificial intelligence, genetic programming, heuristic generation, hyper-heuristics, multidimensional knapsack problem}, year = {2014}, author = {Drake, John H. and Hyde, Matthew and Khaled, Ibrahim and Özcan, Ender} }