dc.contributor.author | Drake, JH | en_US |
dc.contributor.author | Swan, J | en_US |
dc.contributor.author | Neumann, G | en_US |
dc.contributor.author | Özcan, E | en_US |
dc.date.accessioned | 2017-05-10T12:53:06Z | |
dc.date.issued | 2017-01-01 | en_US |
dc.date.submitted | 2017-05-04T04:33:46.428Z | |
dc.identifier.isbn | 9783319554525 | en_US |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/22845 | |
dc.description.abstract | © Springer International Publishing AG 2017. Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. firstor bestfit), 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. | en_US |
dc.format.extent | 189 - 200 | en_US |
dc.rights | This is a pre-copyedited, author-produced version of an article accepted for publication in European Conference on Evolutionary Computation in Combinatorial Optimization following peer review. The version of record is available https://link.springer.com/chapter/10.1007/978-3-319-55453-2_13 | |
dc.title | Sparse, continuous policy representations for uniform online bin packing via regression of interpolants | en_US |
dc.type | Conference Proceeding | |
dc.rights.holder | © Springer International Publishing AG 2017 | |
dc.identifier.doi | 10.1007/978-3-319-55453-2_13 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 10197 LNCS | en_US |
qmul.funder | DAASE: Dynamic Adaptive Automated Software Engineering::Engineering and Physical Sciences Research Council | en_US |