Paper Info
Title
Assessing the performance of several fitness functions in a genetic algorithm for nonlinear separation of sources
Abstract
In this contribution, we propose and analyze three evaluation functions (contrast functions in Independent Component Analysis terminology) for the use in a genetic algorithm (PNL-GABSS, Post-NonLinear Genetic Algorithm for Blind Source Separation) which solves source separation in nonlinear mixtures, assuming the post-nonlinear mixture model. Blind source separation refers to the problem of recovering a set of unknown sources from another set of mixtures directly observable and little more information about the way they were mixed. Assuming statistical independence as the assumption to obtain the original sources we can apply ICA (Independent Component Analysis) as the technique to recover the signals. In order to analyze in practice the performance of the chosen fitness functions in our proposed algorithm, we applied ANOVA (Analysis of Variance) to the results, showing the validity of the three approaches.
Year
DOI
Venue
2005
10.1007/11539902_106
ICNC (3)
Keywords
Field
DocType
genetic algorithm,independent component analysis terminology,independent component analysis,fitness function,post-nonlinear genetic algorithm,unknown source,original source,blind source,nonlinear separation,blind source separation,proposed algorithm,source separation,statistical independence,mixture model,evaluation function
Mathematical optimization,Nonlinear system,Computer science,Algorithm,Fitness function,Independent component analysis,Blind signal separation,Independence (probability theory),Genetic algorithm,Mixture model,Source separation
Conference
Volume
ISSN
ISBN
3612
0302-9743
3-540-28320-X
Citations 
PageRank 
References 
2
0.44
4
Authors
4
Name
Order
Citations
PageRank
F. Rojas11248.09
C. G. Puntonet235434.99
J. M. Górriz357054.40
O. Valenzuela419611.42