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http://dx.doi.org/10.6109/jkiice.2014.18.9.2087

Genetic Algorithm based Orthogonal Matching Pursuit for Sparse Signal Recovery  

Kim, Seehyun (Department of Information and Communications Engineering, The University of Suwon)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.18, no.9, 2014 , pp. 2087-2093 More about this Journal
Abstract
In this paper, an orthogonal matching pursuit (OMP) method combined with genetic algorithm (GA), named GAOMP, is proposed for sparse signal recovery. Some recent greedy algorithms such as SP, CoSaMP, and gOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. However they still often fail to converge to the solution because the support set could not avoid the local minimum during the iterations. Mutating the candidate support set chosen by the OMP algorithm, GAOMP is able to escape from the local minimum and hence recovers the sparse signal. Experimental results show that GAOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.
Keywords
genetic algorithm (GA); greedy algorithm; orthogonal matching pursuit (OMP); sparse signal reconstruction;
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