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dc.contributor.authorDick, Granten_NZ
dc.date.available2011-04-07T03:02:05Z
dc.date.copyright2006-11en_NZ
dc.identifier.citationDick, G. (2006). Evolutionary multiobjective optimisation through spatially-structured non-dominated sorting: a preliminary study (pp. 87–96). Presented at the 18th Annual Colloquium of the Spatial Information Research Centre (SIRC 2006: Interactions and Spatial Processes).en
dc.identifier.urihttp://hdl.handle.net/10523/739
dc.description.abstractMultiobjective evolutionary algorithms (MOEAs) are useful tools capable of searching problems that contain several conflicting criteria. Although MOEAs have been shown to be capable of finding a wide spread of Pareto-optimal solutions for a given problem, they are still hindered by the requirement for significant computation. This paper investigates a new MOEA that incorporates spatial structure into the population. The introduction of space into the algorithm alters the behaviour of the algorithm so that computational complexity increases linearly with population size. In addition, the paper suggests paths that could be taken to improve the algorithm’s ability to successfully converge upon the global Pareto-optimal front of a given problem.en_NZ
dc.format.mimetypeapplication/pdf
dc.relation.urihttp://www.business.otago.ac.nz/sirc/conferences/2006/19_Dick.pdfen_NZ
dc.subjectmultiobjective optimisationen_NZ
dc.subjectnon-dominated sortingen_NZ
dc.subjectpopulation structureen_NZ
dc.subjectcomputational complexityen_NZ
dc.subjectEvolutionary Algorithmsen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleEvolutionary multiobjective optimisation through spatially-structured non-dominated sorting: a preliminary studyen_NZ
dc.typeConference or Workshop Item (Paper)en_NZ
dc.description.versionPublisheden_NZ
otago.date.accession2007-05-29en_NZ
otago.relation.pages87-96en_NZ
otago.openaccessOpen
dc.identifier.eprints696en_NZ
dc.description.refereedNon Peer Revieweden_NZ
otago.school.eprintsSpatial Information Research Centreen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.referencesDeb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons. Chichester, UK. Deb, K., Agrawal, S., Pratap, A. & Meyarivan, T. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II” IEEE Trans. Evolutionary Computation. 6(2). Dick, G. & Whigham, P. A. (2005). “The behaviour of genetic drift in a spatially-structured evolutionary algo- rithm” In D. Corne, Z. Michalewicz, B. McKay, G. Eiben, D. Fogel, C. Fonseca, G. Greenwood, G. Raidl, K. C. Tan & A. Zalzala (eds), Proceedings of the 2005 IEEE Congress on Evolutionary Computation. Vol. 2 IEEE Press Edinburgh, Scotland, UK pp. 1855–1860. Kirley, M. (2001). “MEA: A metapopulation evolutionary algorithm for multi-objective optimisation problems” Proceedings of the 2001 IEEE Conference on Evolutionary Computation. IEEE Press Seoul, Korea. Murata, T., Ishibuchi, H. & Gen, M. (2000). “Cellular Genetic Local Search for Multi-Objective Optimization” In D. Whitley, D. Goldberg, E. Cantu-Paz, L. Spector, I. Parmee & H.-G. Beyer(eds), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000). Morgan Kaufmann Las Vegas,Nevada, USA pp. 307–314. Sarma, J.(1998). An Analysis of Decentralized and Spatially Distributed Genetic Algorithms. PhD thesis George Mason University Fairfax VA, USA. Tomassini, M.(2005). Spatially structured evolutionary algorithms.Springer. Zitzler, E., Deb, K. & Thiele, L. (2000). “Comparisonof Multiobjective Evolutionary Algorithms: Empirical Results” Evolutionary Computation. 8(2):173–195.en_NZ
otago.event.dates6-7 November 2006en_NZ
otago.event.placeDunedin, New Zealanden_NZ
otago.event.typeconferenceen_NZ
otago.event.title18th Annual Colloquium of the Spatial Information Research Centre (SIRC 2006: Interactions and Spatial Processes)en_NZ
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