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dc.contributor.authorEtschberger, Stefanen_US
dc.contributor.authorHilbert, Andreasen_US
dc.date.accessioned2009-01-29T15:09:28Z-
dc.date.available2009-01-29T15:09:28Z-
dc.date.issued2002en_US
dc.identifier.urihttp://hdl.handle.net/10419/22817-
dc.description.abstractMultidimensional scaling is very common in exploratory data analysis. It is mainly used to represent sets of objects with respect to their proximities in a low dimensional Euclidean space. Widely used optimization algorithms try to improve the representation via shifting its coordinates in direction of the negative gradient of a corresponding fit function. Depending on the initial configuration, the chosen algorithm and its parameter settings there is a possibility for the algorithm to terminate in a local minimum. This article describes the combination of an evolutionary model with a non-metric gradient solution method to avoid this problem. Furthermore a simulation study compares the results of the evolutionary approach with one classic solution method.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseriesArbeitspapiere zur mathematischen Wirtschaftsforschung 181en_US
dc.subject.ddc330en_US
dc.subject.stwHeuristisches Verfahrenen_US
dc.subject.stwMathematische Optimierungen_US
dc.subject.stwTheorieen_US
dc.titleMultidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minimaen_US
dc.typeWorking Paperen_US
dc.identifier.ppn379949377en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:augamw:181-
Appears in Collections:Arbeitspapiere zur mathematischen Wirtschaftsforschung, Universität Augsburg

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