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Parallel Genetic Algorithm Decoder Based on Multi-Criteria Method for Low Density Parity Check Codes

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Genetic algorithms are successfully used for decoding some classes of error correcting codes, and to give very good performances when solving large optimization problems. This article proposes a decoder based on parallel Genetic Algorithms (PGAD) for Decoding Low Density Parity Check (LDPC) codes. The proposed algorithm gives large gains over the Sum-Product decoder, which proves its efficiency; the best performances are obtained for Ring Crossover (RC) as a type of crossover and the tournament as a type of selection. Furthermore, the performances of the new decoder are improved using Multi-criteria method, this new optimized version called (PGAWS).Simulation results show that our proposed PGAD exceeds the sum-product performance by a gain of 1.5 dB at BER = 10-4, and the PGAWS exceeds the sum-product performance by 2.5 dB.

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Parallel Genetic Algorithms Decoder; Fitness Function; Sum-Product Decoder; LDPC Codes; Multi-Criteria Method; Weighted Sum Method; Error Correcting Codes

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