Assessing the Performance of Several Fitness Functions in a Genetic Algorithm for Nonlinear Separation of Sources

Abstract

AbstractIn 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.KeywordsGenetic AlgorithmMutual InformationFitness FunctionIndependent Component AnalysisSource SeparationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.