Arbeitspapier
Multidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minima
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Arbeitspapiere zur mathematischen Wirtschaftsforschung ; No. 181
- Klassifikation
-
Wirtschaft
- Thema
-
Heuristisches Verfahren
Mathematische Optimierung
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Etschberger, Stefan
Hilbert, Andreas
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Augsburg, Institut für Statistik und Mathematische Wirtschaftstheorie
- (wo)
-
Augsburg
- (wann)
-
2002
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Etschberger, Stefan
- Hilbert, Andreas
- Universität Augsburg, Institut für Statistik und Mathematische Wirtschaftstheorie
Entstanden
- 2002
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