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Institut für Statistik und Mathematische Wirtschaftstheorie, Universität Augsburg >
Arbeitspapiere zur mathematischen Wirtschaftsforschung, Universität Augsburg >
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http://hdl.handle.net/10419/22817
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| Title: | | Multidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minima  |
| Authors: | | Etschberger, Stefan Hilbert, Andreas |
| Issue Date: | | 2002 |
| Series/Report no.: | | Arbeitspapiere zur mathematischen Wirtschaftsforschung 181 |
| 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. |
| Document Type: | | Working Paper |
| Appears in Collections: | | Arbeitspapiere zur mathematischen Wirtschaftsforschung, Universität Augsburg
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