Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26957
Appears in Collections:Computing Science and Mathematics Technical Reports
Peer Review Status: Unrefereed
Title: Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features (Detailed Experiments and Results)
Author(s): Brownlee, Alexander
Woodward, John R
Veerapen, Nadarajen
Contact Email: alexander.brownlee@stir.ac.uk
Citation: Brownlee A, Woodward JR & Veerapen N (2018) Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features (Detailed Experiments and Results). Not applicable. Stirling: University of Stirling.
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
Keywords: Automatic design of algorithms
features
bin packing
Issue Date: 7-Apr-2018
Publisher: University of Stirling
Abstract: Automatic Design of Algorithms (ADA) shifts the burden of algorithm choice and design from developer to machine. Constructing an appropriate solver from a set of problem instances becomes a machine learning problem, with instances as training data. An efficient solver is trained for unseen problem instances with similar characteristics to those in the training set. However, this paper reveals that, as with classification and regression, for ADA not all training sets are equally valuable. We apply a typical genetic programming ADA approach for bin packing problems to several new and existing public benchmark sets. Algorithms trained on some sets are general and apply well to most others, whereas some training sets result in highly specialised algorithms that do not generalise. We relate these findings to features (simple metrics) of instances. Using instance sets with narrowly-distributed features for training results in highly specialised algorithms, whereas those with well-spread features result in very general algorithms. We show that variance in certain features has a strong correlation with the generality of the trained policies. Our results provide further grounding for recent work using features to predict algorithm performance, and show the suitability of particular instance sets for training in ADA for bin packing. The data sets, including all computed features, the evolved policies, and their performances, and the visualisations for all feature sets, are available from http://hdl.handle.net/11667/108.
Type: Technical Report
URI: http://hdl.handle.net/1893/26957
Affiliation: Computing Science
Queen Mary, University of London
Computing Science

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