Evolutionary algorithm for compressive sensing
by Uday K. Chakraborty
International Journal of Automation and Control (IJAAC), Vol. 9, No. 1, 2015

Abstract: This paper presents a non-traditional approach to compressive sensing, by developing an evolutionary algorithm-based method for signal reconstruction. Previous work in signal reconstruction in compressive sensing focused primarily on framing the problem as a convex optimisation that minimises the 1 norm of the signal. Minimising the 2 norm does not help as it leads to a non-sparse solution. Minimising the 0 norm is known to be NP-complete, thereby requiring exhaustive enumeration which is computationally prohibitive. Our approach is different from the methods adopted in the literature and is capable of handling 0-, 1- or 2-norm minimisation, and linear or nonlinear combinations thereof, in the same framework, yielding fairly good signal recovery with high probability. We provide empirical results on a number of test problems.

Online publication date: Sun, 15-Mar-2015

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