Automating the packing heuristic design process with genetic programming
The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which metho...
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| Format: | Article |
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MIT Press
2012
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| Online Access: | https://eprints.nottingham.ac.uk/47386/ |
| _version_ | 1848797533062561792 |
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| author | Burke, Edmund K. Hyde, Matthew R. Kendall, Graham Woodward, John |
| author_facet | Burke, Edmund K. Hyde, Matthew R. Kendall, Graham Woodward, John |
| author_sort | Burke, Edmund K. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains. |
| first_indexed | 2025-11-14T20:05:23Z |
| format | Article |
| id | nottingham-47386 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:05:23Z |
| publishDate | 2012 |
| publisher | MIT Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-473862020-05-04T16:32:24Z https://eprints.nottingham.ac.uk/47386/ Automating the packing heuristic design process with genetic programming Burke, Edmund K. Hyde, Matthew R. Kendall, Graham Woodward, John The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains. MIT Press 2012-02-23 Article PeerReviewed Burke, Edmund K., Hyde, Matthew R., Kendall, Graham and Woodward, John (2012) Automating the packing heuristic design process with genetic programming. Evolutionary Computation, 20 (1). pp. 63-89. ISSN 1530-9304 Genetic programming; genetic algorithms; evolutionary design; cutting and packing; hyper-heuristics http://www.mitpressjournals.org/doi/10.1162/EVCO_a_00044 doi:10.1162/EVCO_a_00044 doi:10.1162/EVCO_a_00044 |
| spellingShingle | Genetic programming; genetic algorithms; evolutionary design; cutting and packing; hyper-heuristics Burke, Edmund K. Hyde, Matthew R. Kendall, Graham Woodward, John Automating the packing heuristic design process with genetic programming |
| title | Automating the packing heuristic design process with genetic programming |
| title_full | Automating the packing heuristic design process with genetic programming |
| title_fullStr | Automating the packing heuristic design process with genetic programming |
| title_full_unstemmed | Automating the packing heuristic design process with genetic programming |
| title_short | Automating the packing heuristic design process with genetic programming |
| title_sort | automating the packing heuristic design process with genetic programming |
| topic | Genetic programming; genetic algorithms; evolutionary design; cutting and packing; hyper-heuristics |
| url | https://eprints.nottingham.ac.uk/47386/ https://eprints.nottingham.ac.uk/47386/ https://eprints.nottingham.ac.uk/47386/ |