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dc.contributor.authorWood, M
dc.contributor.authorEames, ME
dc.date.accessioned2018-03-09T14:28:00Z
dc.date.issued2017-08
dc.description.abstractNon-dominated sorting genetic algorithm version 2 (NSGA-II) is a multi-objective optimisation method. NSGA-II is often used to optimise the design of building. This paper details small improvements to this algorithm using ‘fitness approximation’ methods. Fitness approximation is used speed up the conversion of NSGA-II. Radial basis functions networks have been shown to be useful for this. Although there are many types of fitness approximation function that could be use for this purpose, Kriging methods have not yet been tested. In this paper, Kriging models are compared to standard NSGA-II. The results show that Kriging-based fitness approximation slightly improves upon standard NSGAII. More work is needed to test this method on different building types as well as more complex problems, such as those associated with HVAC design.en_GB
dc.description.sponsorshipThe authors would like to thank for EPSRC for funding this research [Ref: EP/M021890/1]en_GB
dc.identifier.citationBuilding Simulation 2017, 15th International Conference of IBPSA, 7-9 August 2017, San Francisco, USAen_GB
dc.identifier.urihttp://hdl.handle.net/10871/32018
dc.publisherInternational Building Performance Simulation Association ((IBPSA)en_GB
dc.rights© 2017 IBPSAen_GB
dc.titleUsing Kriging regression to improve the stability and diversity in NSGA-IIen_GB
dc.typeConference paperen_GB
dc.date.available2018-03-09T14:28:00Z
dc.descriptionThis is the author accepted manuscript. The final version is available from IBPSAen_GB


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