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Sparse Signal Recovery based on Hybrid Genetic Algorithm

Author Affiliations

  • 1Department of Electronic Engineering, Faculty of Engineering and Technology International Islamic University Islamabad, PAKISTAN
  • 2 Department of Electronic Engineering, Air University, Institute of Signals, Systems and Soft computing (ISSS), Islamabad, PAKISTAN
  • 3 Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad, PAKISTAN

Res. J. Recent Sci., Volume 3, Issue (5), Pages 86-93, May,2 (2014)


This paper introduces a novel technique of recovering an S-sparse signal from lesser number of random measurements (under sampled signal) using hybrid genetic algorithm (GA). The proposed method uses a chromosome-based cross over to produce the off springs with better fitness. The l_0minimizationconstraint is directly incorporated in population to achieve the desired sparsity level. The slow and premature convergence of GA is prevented with the help of modified parallel coordinate descent (PCD) algorithm. This hybrid mechanism of GA along with modified PCD makes the sparse signal recovery faster and accurate. The performance of the proposed algorithm is verified by comparing its results with those of PCD and separable surrogate functional (SSF) algorithms. The effectiveness of hybrid GA is further demonstrated experimentally by faithfully recovering a synthetic one-dimensional sparse signal. The simulation results show that an accurate sparse signal reconstruction is possible with hybrid GA using only a small number of observations.


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