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A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem (2012)
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BibTeX
@MISC{Drake12agenetic,
author = {John H. Drake and Matthew Hyde and Khaled Ibrahim and Ender Özcan},
title = {A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem},
year = {2012}
}
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Abstract
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. Early hyperheuristics focussed 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. This work investigates the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results.
Keyphrases
genetic programming hyper-heuristic multidimensional knapsack problem genetic programming search space constructive heuristic hyper-heuristic research test problem unseen instance new heuristic standard benchmark heuristic component recent trend knapsack item abstract hyper-heuristics high-level search technique human-competitive result early hyperheuristics multidimensional 0-1 knapsack problem low-level heuristic hyper-heuristic methodology
