Efficiency and Performance Analysis between Genetic Algorithm and Parallel Quantum Genetic Algorithm through the Density Classification and the Knapsack Problems
Zakaria Laboudi, Salim Chikhi
Journal of Networking Technology, Vol. 1, No. 4, 2010.
DOI: 
Link: 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.