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dc.contributor.authorDick, Granten_NZ
dc.date.available2011-04-07T03:02:12Z
dc.date.copyright2005-11en_NZ
dc.identifier.citationDick, G. (2005). A comparison of localised and global niching methods (pp. 91–101). Presented at the 17th Annual Colloquium of the Spatial Information Research Centre (SIRC 2005: A Spatio-temporal Workshop).en
dc.identifier.urihttp://hdl.handle.net/10523/767
dc.description.abstractNiching methods are a useful extension of evolutionary computation that allow evolutionary algorithms to be applied in multimodal problem domains. Current niching methods use either one of two methods to promote the formation of species within a population. Genetics-based methods, such as fitness sharing or clearing, work directly on the search space of the problem. Alternatively, spatiallystructured evolutionary algorithms are used to place individuals onto a landscape and restrict mating to within isolated demes of population members. This isolation promotes the driving of geographically distant individuals towards separate parts of the search space. This paper introduces the concept of localised niching (LC). LC takes the traditionally global operations used in genetics-based niching and applies them locally in a spatially-structured population. Testing on two well known and difficult benchmark problems indicates that LC not only has the potential to significantly outperform the traditional global niching methods, but is also more resistant to some of the known limitations of genetics-based species formation.en_NZ
dc.format.mimetypeapplication/pdf
dc.relation.urihttp://www.business.otago.ac.nz/SIRC05/conferences/2005/11_dick.pdfen_NZ
dc.subjectmultimodal optimisationen_NZ
dc.subjectspatially-structured populationsen_NZ
dc.subjectniching methodsen_NZ
dc.subjectEvolutionary Algorithmsen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleA comparison of localised and global niching methodsen_NZ
dc.typeConference or Workshop Item (Paper)en_NZ
dc.description.versionPublisheden_NZ
otago.date.accession2006-08-10en_NZ
otago.relation.pages91-101en_NZ
otago.openaccessOpen
dc.identifier.eprints348en_NZ
dc.description.refereedNon Peer Revieweden_NZ
otago.school.eprintsSpatial Information Research Centreen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.referencesBessaou, M., P´etrowski, A. & Siarry, P. (2000). “Island Model cooperating with Speciation for Multimodal Optimization” In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo & H.-P. Schwefel (eds), Parallel Problem Solving from Nature – PPSN VI. Springer Berlin pp. 437–446. Collins, R. J. & Jefferson, D. R. (1991). “Selection in Massively Parallel Genetic Algorithms” In R. K. Belew & L. B. Booker (eds), Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann Publishers San Mateo, CA. Darwen, P. J. & Yao, X. (1995). “A Dilemma for Fitness Sharing with a Scaling Function” Proceedings of the Second IEEE International Conference on Evolutionary Computation. IEEE Press Piscataway, New Jersey. Davidor, Y. (1991). “A Naturally Occuring Niche & Species Phenomenon: The Model and First Results” In R. K. Belew & L. B. Booker (eds), Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA’91). Morgan Kaufmann Publishers San Mateo, California pp. 257–263. DeJong, K. A. (1975). An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis University of Michigan Ann Arbor, MI. Dissertation Abstracts International 36(10), 5140B, University Microfilms Number 76-9381. DeJong, K. & Sarma, J. (1995). “On Decentralizing Selection Algorithms” In L. J. Eshelman (ed.), Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA’95). Morgan Kaufmann Publishers San Francisco, California pp. 17–23. Dick, G. & Whigham, P. (2005). “The Behaviour of Genetic Drift in a Spatially-Structured Evolutionary Algorithm” 2005 IEEE Congress on Evolutionary Computation. IEEE Press. pp. 1855–1860. Goldberg, D. E., Deb, K. & Horn, J. (1992). “Massive Multimodality, Deception, and Genetic Algorithms” In R. M¨anner & B. Manderick (eds), Parallel Problem Solving from Nature, 2. Elsevier Science Publishers, B. V. Amsterdam pp. 37–46.en_NZ
otago.event.dates24-25 November 2005en_NZ
otago.event.placeDunedin, New Zealanden_NZ
otago.event.typeconferenceen_NZ
otago.event.title17th Annual Colloquium of the Spatial Information Research Centre (SIRC 2005: A Spatio-temporal Workshop)en_NZ
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