Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23148
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Brownlee, Alexander
Contact Email: sbr@cs.stir.ac.uk
Title: Mining Markov Network Surrogates for Value-Added Optimisation
Editors: Friedrich, T
Citation: Brownlee A (2016) Mining Markov Network Surrogates for Value-Added Optimisation, Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, Genetic and Evolutionary Computation Conference GECCO’16, Denver, CO, USA, 20.7.2016 - 24.7.2016, New York: ACM, pp. 1267-1274.
Issue Date: 2016
Conference Name: Genetic and Evolutionary Computation Conference GECCO’16
Conference Dates: 2016-07-20T00:00:00Z
Conference Location: Denver, CO, USA
Abstract: Surrogate fitness functions are a popular technique for speeding up metaheuristics, replacing calls to a costly fitness function with calls to a cheap model. However, surrogates also represent an explicit model of the fitness function, which can be exploited beyond approximating the fitness of solutions. This paper proposes that mining surrogate fitness models can yield useful additional information on the problem to the decision maker, adding value to the optimisation process. An existing fitness model based on Markov networks is presented and applied to the optimisation of glazing on a building facade. Analysis of the model reveals how its parameters point towards the global optima of the problem after only part of the optimisation run, and reveals useful properties like the relative sensitivities of the problem variables.
Type: Conference Paper
Status: Book Chapter: publisher version
Rights: Published in Companion Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2016. The definitive version of record can be found in GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, Pages 1267-1274 ISBN 978-1-4503-2138-9. DOI: 10.1145/2908961.2931711
URI: http://hdl.handle.net/1893/23148
URL: http://dl.acm.org/citation.cfm?id=2931711
Affiliation: Computing Science - CSM Dept

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