Conference Proceeding

Mining Markov Network Surrogates for Value-Added Optimisation



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. Genetic and Evolutionary Computation Conference GECCO’16, Denver, CO, USA, 20.07.2016-24.07.2016. New York: ACM, pp. 1267-1274.

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.

metaheuristics; surrogates; fitness approximation; decision making

FundersEngineering and Physical Sciences Research Council and Engineering and Physical Sciences Research Council
Publication date31/12/2016
Publication date online31/07/2016
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Place of publicationNew York
ConferenceGenetic and Evolutionary Computation Conference GECCO’16
Conference locationDenver, CO, USA

People (1)


Dr Sandy Brownlee
Dr Sandy Brownlee

Senior Lecturer in Computing Science, Computing Science and Mathematics - Division