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

Burke, Edmund K.; Hyde, Matthew; Kendall, Graham; Woodward, John


Edmund K. Burke

Matthew Hyde

Graham Kendall

John Woodward


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.


Burke, E. K., Hyde, M., Kendall, G., & Woodward, J. (2010). A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics. IEEE Transactions on Evolutionary Computation, 14(6),

Journal Article Type Article
Acceptance Date Dec 4, 2009
Online Publication Date Jun 21, 2010
Publication Date Dec 31, 2010
Deposit Date Nov 2, 2017
Journal IEEE Transactions on Evolutionary Computation
Print ISSN 1089-778X
Electronic ISSN 1089-778X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 14
Issue 6
Keywords 2-D stock cutting; genetic programming; hyper-heuristics
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