Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An evolutionary approach for blind inversion of wiener systems
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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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.