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Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10197))


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.

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    The term ‘locality’ is often used in this context in evolutionary computation.

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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.

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