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https://hdl.handle.net/10419/22817
Kompletter Metadatensatz
DublinCore-Feld | Wert | Sprache |
---|---|---|
dc.contributor.author | Etschberger, Stefan | en |
dc.contributor.author | Hilbert, Andreas | en |
dc.date.accessioned | 2009-01-29T15:09:28Z | - |
dc.date.available | 2009-01-29T15:09:28Z | - |
dc.date.issued | 2002 | - |
dc.identifier.uri | http://hdl.handle.net/10419/22817 | - |
dc.description.abstract | Multidimensional 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 |
dc.language.iso | eng | en |
dc.publisher | |aUniversität Augsburg, Institut für Statistik und Mathematische Wirtschaftstheorie |cAugsburg | en |
dc.relation.ispartofseries | |aArbeitspapiere zur mathematischen Wirtschaftsforschung |x181 | en |
dc.subject.ddc | 330 | en |
dc.subject.stw | Heuristisches Verfahren | en |
dc.subject.stw | Mathematische Optimierung | en |
dc.subject.stw | Theorie | en |
dc.title | Multidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minima | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 379949377 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
dc.identifier.repec | RePEc:zbw:augamw:181 | en |
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