Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27082
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Brownlee, Alexander
Woodward, John R
Veerapen, Nadarajen
Contact Email: alexander.brownlee@stir.ac.uk
Title: Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features
Citation: Brownlee A, Woodward JR & Veerapen N (2018) Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features. In: Proceedings of GECCO 2018. Genetic and Evolutionary Computation Conference 2018, 15.07.2018-19.07.2018. New York: ACM, pp. 135-136. https://doi.org/10.1145/3205651.3205748
FAIME: A Feature based Framework to Automatically Integrate and Improve Metaheuristics via Examples.
EP/N002849/1
DAASE: Dynamic Adaptive Automated Software Engineering
EP/J017515/1
Issue Date: 31-Dec-2018
Conference Name: Genetic and Evolutionary Computation Conference 2018
Conference Dates: 2018-07-15 - 2018-07-19
Conference Location: New York
Abstract: Automatic Design of Algorithms (ADA) treats algorithm choice and design as a machine learning problem, with problem instances as training data. However, this paper reveals that, as with classification and regression, for ADA not all training sets are equally valuable. We apply genetic programming ADA for bin packing to sev- eral new and existing benchmark sets. Using sets with narrowly- distributed features for training results in highly specialised al- gorithms, whereas those with well-spread features result in very general algorithms. Variance in certain features has a strong corre- lation with the generality of the trained policies.
Status: AM - Accepted Manuscript
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Publisher policy allows this work to be made available in this repository. Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion by ACM. The original publication is available at: https://doi.org/10.1145/3205651.3205748

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