A Canonical Genetic Algorithm for Blind Inversion of Linear Channels

Abstract

Abstract.It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA is justified in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics.

An immune-inspired, information-theoretic framework for blind inversion of Wiener systems

“…As an alternative to gradient-based algorithms, Rojas et al [16,23] employ a genetic algorithm (GA) [24] to perform the task of searching for the optimal parameters. The nonlinear part of the Hammerstein system is represented by a polynomial composed exclusively of monomials of odd power, and, in view of the fact that the theoretical separability condition [5] requires that gðf ðÁÞÞ be a monotonic function, all of its coefficients are constrained to be positive.…”