Edmund K. Burke email@example.com
A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics
Burke, Edmund K.; Hyde, Matthew; Kendall, Graham; Woodward, John
Matthew Hyde firstname.lastname@example.org
Graham Kendall email@example.com
John Woodward firstname.lastname@example.org
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), doi:10.1109/tevc.2010.2041061
|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|
|Publisher||Institute of Electrical and Electronics Engineers|
|Peer Reviewed||Peer Reviewed|
|Keywords||2-D stock cutting; genetic programming; hyper-heuristics|
This file is under embargo due to copyright reasons.
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