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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Brownlee, Alexander
Woodward, John R
Veerapen, Nadarajen
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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.
FAIME: A Feature based Framework to Automatically Integrate and Improve Metaheuristics via Examples.
DAASE: Dynamic Adaptive Automated Software Engineering
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
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