Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26457
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Drake, John H
Swan, Jerry
Neumann, Geoffrey
Ozcan, Ender
Title: Sparse, continuous policy representations for uniform online bin packing via regression of interpolants
Editor(s): Hu, B
Lopez-Ibanez, M
Citation: Drake JH, Swan J, Neumann G & Ozcan E (2017) Sparse, continuous policy representations for uniform online bin packing via regression of interpolants In: Hu B, Lopez-Ibanez M (ed.) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2017, Cham, Switzerland: Springer. European Conference on Evolutionary Computation in Combinatorial Optimization: EvoCOP 2017, 19.4.2017 - 21.4.2017, Amsterdam, The Netherlands, pp. 189-200.
Issue Date: 2017
Series/Report no.: Lecture Notes in Computer Science, 10197
Conference Name: European Conference on Evolutionary Computation in Combinatorial Optimization: EvoCOP 2017
Conference Dates: 2017-04-19T00:00:00Z
Conference Location: Amsterdam, The Netherlands
Abstract: Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. first- or best- fit), there has been interest over the last decade in the automatic generation of policies. One of the main limitations of some previously-used policy representations is the trade-off between locality and granularity in the associated search space. In this article, we adopt an interpolation-based representation which has the jointly-desirable properties of being sparse and continuous (i.e. exhibits good genotype-to-phenotype locality). In contrast to previous approaches, the policy space is searchable via real-valued optimization methods. Packing policies using five different interpolation methods are comprehensively compared against a range of existing methods from the literature, and it is determined that the proposed method scales to larger instances than those in the literature.
Status: Book Chapter: author post-print (pre-copy editing)
Rights: Publisher policy allows this work to be made available in this repository.Drake J.H., Swan J., Neumann G., Özcan E. (2017) Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants. In: Hu B., López-Ibáñez M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2017. Lecture Notes in Computer Science, vol 10197. Springer, Cham. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-55453-2_13
URL: https://link.springer.com/chapter/10.1007/978-3-319-55453-2_13

Files in This Item:
File Description SizeFormat 
sparse-continuous-policy.pdf299.85 kBAdobe PDFView/Open



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.