Open Science Research Excellence
@article{(International Science Index):,
  title    = {Metaheuristic Algorithms for Decoding Binary Linear Codes},
  author    = {Hassan Berbia and  Faissal Elbouanani and  Rahal Romadi and  Mostafa Belkasmi},
  country   = {},
  abstract  = {This paper introduces two decoders for binary linear
codes based on Metaheuristics. The first one uses a genetic algorithm
and the second is based on a combination genetic algorithm with
a feed forward neural network. The decoder based on the genetic
algorithms (DAG) applied to BCH and convolutional codes give good
performances compared to Chase-2 and Viterbi algorithm respectively
and reach the performances of the OSD-3 for some Residue
Quadratic (RQ) codes. This algorithm is less complex for linear
block codes of large block length; furthermore their performances
can be improved by tuning the decoder-s parameters, in particular the
number of individuals by population and the number of generations.
In the second algorithm, the search space, in contrast to DAG which
was limited to the code word space, now covers the whole binary
vector space. It tries to elude a great number of coding operations
by using a neural network. This reduces greatly the complexity of
the decoder while maintaining comparable performances.},
    journal   = {International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering},  volume    = {5},
  number    = {4},
  year      = {2011},
  pages     = {572 - 578},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 52, 2011},