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Sparse, continuous policy representations for uniform online bin packing via regression of interpolants

Swan, Jerry; Drake, John H.; Neumann, Geoff; Özcan, Ender

Authors

Jerry Swan

John H. Drake

Geoff Neumann



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.

Journal Article Type Article
Publication Date Apr 19, 2017
Journal Lecture Notes in Computer Science
Electronic ISSN 0302-9743
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 10197
APA6 Citation Swan, J., Drake, J. H., Neumann, G., & Özcan, E. (2017). Sparse, continuous policy representations for uniform online bin packing via regression of interpolants. Lecture Notes in Artificial Intelligence, 10197, https://doi.org/10.1007/978-3-319-55453-2_13
DOI https://doi.org/10.1007/978-3-319-55453-2_13
Keywords Hyper-heuristics, Online Bin Packing, CMA-ES, Heuristic Generation, Sparse Policy Representations, Metaheuristics, Optimisation
Publisher URL http://link.springer.com/chapter/10.1007%2F978-3-319-55453-2_13
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information 17th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2017), Amsterdam, Netherlands, 19-21 April 2017.

The final publication is available at link.springer.com.

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, pp. 189-200.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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