Genetic Algorithm Visualization Using Self-organizing Maps

Authors:
Gustavo Romero;Juan J. Merelo Guervós;Pedro A. Castillo Valdivieso;J. G. Castellano;Maribel García Arenas
Affiliations:
-;-;-;-;-
Venue:
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Year:
2002

Citing 4
Cited 1

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
A Nonlinear Mapping for Data Structure Analysis

IEEE Transactions on Computers
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets

IEEE Transactions on Neural Networks

Intelligent exploration for genetic algorithms: using self-organizing maps in evolutionary computation

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.