A genetic programming hyper-heuristic for the multidimensional knapsack problem

作者：Drake John H^*; Hyde Matthew; Ibrahim Khaled; Ozcan Ender

来源：Kybernetes, 2014, 43(9-10): 1500-1511.

DOI：10.1108/K-09-2013-0201

• Artificial intelligence
• Genetic programming
• Heuristic generation
• Hyper-heuristics
• Multidimensional knapsack problem

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.

• 出版日期2014