@techreport{Etschberger2002Multidimensional,
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.},
author = {Stefan Etschberger and Andreas Hilbert},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {330; Heuristisches Verfahren; Mathematische Optimierung; Theorie},
language = {eng},
number = {181},
title = {Multidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minima},
type = {Arbeitspapiere zur mathematischen Wirtschaftsforschung},
url = {http://hdl.handle.net/10419/22817},
year = {2002}
}
