@article{408,
  author = {Zakaria Laboudi, Salim Chikhi},
  title = {Efficiency and Performance Analysis between Genetic Algorithm and Parallel Quantum Genetic Algorithm through the Density Classification and the Knapsack Problems},
  journal = {Journal of Networking Technology},
  year = {2010},
  volume = {1},
  number = {4},
  doi = {},
  url = {http://www.dline.info/jnt/fulltext/v1n3/5.pdf},
  abstract = {Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative of evolutionary algorithms was invented: quantum genetic algorithms (QGA). In this paper, we outline the approach of QGA by giving a comparison with conventional genetic algorithm (CGA). We have executed our algorithm for 100 generations. Each CA in the population was iterated over M = 150 iterations. We have compared our best fitness obtained with the best that exists in the literature. Our parallel algorithm was run on a local network of 10 sites: a master site and nine slaves. The results have shown that QGA can be a very promising tool for exploring search spaces with a high performance and efficiency.},
}