Multidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minima - Stefan Etschberger; Andreas Hilbert (Universität Augsburg, Institut für Statistik und Mathematische Wirtschaftstheorie, 2002)
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.
Keywords: 330; Heuristisches Verfahren; Mathematische Optimierung; Theorie