Genetic Algorithm Visualization Using Self-organizing Maps
This paper gives an overview of evolutionary computation visualization and describes the application of visualization to some well known multidimensional problems. Self-Organizing Maps (SOM) are used for multidimensional scaling and projection. We show how different ways of training the SOM make it more or less adequate for the visualization task.
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- 3.Andreas König. A survey of methods for multivariate data projection, visualisation and interactive analysis. In Proc. of the 5th International Conference on Soft Computing and Information/Intelligent Systems (IIZUKA’98), pages 55–59, October 1998.Google Scholar
- 4.Christopher M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995.Google Scholar
- 5.J. W. Sammon Jr. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, pages 401–409, 1969.Google Scholar
- 7.Nicolas Donckers Michael Verleysen John Aldo Lee, Amaury Lendasse. A robust nonlinear projection method. In European Symposium on Artifial Neural Networks (ESANN2000), pages 13–20, April 2000.Google Scholar
- 8.S. Spangler D. S. Modha and S. Vaithyanathan. Multidimensional cluster visualization using guided tours. Technical Report Reseach Report RJ 10124, IBM Almaden Research Center, San Jose, CA, June 17 1998.Google Scholar
- 9.T. Kohonen. The Self-Organizing Map. In Proceedings of the IEEE, volume 78, pages 1464–1480, 1990.Google Scholar