A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics

Burke, Edmund K., Hyde, Matthew, Kendall, Graham and Woodward, John (2010) A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics. IEEE Transactions on Evolutionary Computation, 14 (6). pp. 942-958. ISSN 1089-778X

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We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This paper contributes to a growing research area that represents a paradigm shift in search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. A key motivation is to investigate methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods. However, the task of intelligently searching through all of the potential combinations of these components is better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. This paper shows that a GP hyper-heuristic can be employed to automatically generate human competitive heuristics in a very-well studied problem domain.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/707012
Keywords: 2-D stock cutting; genetic programming; hyper-heuristics
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1109/tevc.2010.2041061
Depositing User: LIN, Zhiren
Date Deposited: 02 Nov 2017 08:35
Last Modified: 29 Apr 2020 14:55
URI: https://eprints.nottingham.ac.uk/id/eprint/47471

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