Meta-heuristics hybridizing independent component analysis with genetic algorithms

Abstract

We present a novel method for blindly separating unobservable independent component signals from their linear mixtures, using meta-heuristics such as genetic algorithms (GA) to minimize the nonconvex and nonlinear cost functions. This approach is very useful in many fields such as forecasting indexes in financial stock markets, where the search for independent components is the major task to include exogenous information into the learning machine. The presented GA is able to extract independent components at a faster rate than the previous independent component analysis algorithms based on higher order statistics (HOS), showing significant accuracy and robustness as the input space dimension increases.