|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||Mining Markov Network Surrogates for Value-Added Optimisation|
|Citation:||Brownlee A (2016) Mining Markov Network Surrogates for Value-Added Optimisation In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, New York: ACM. Genetic and Evolutionary Computation Conference GECCO’16, 20.7.2016 - 24.7.2016, Denver, CO, USA, pp. 1267-1274.|
|Conference Name:||Genetic and Evolutionary Computation Conference GECCO’16|
|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.|
|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|
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