The International Arab Journal of Information Technology (IAJIT)

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Comparison of Genetic Algorithm and Quantum Genetic Algorithm

Evolving solutions rather than computing them certa inly 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 Convent ional Genetic Algorithm (CGA). Our results have shown that QGA can be a very promising tool for exploring search space s.


[1] Draa A., Meshoul S., Talbi H., and Batouche M., A Quantum6Inspired Differential Evolution Algorithm for Solving the N6Queens Problem, The International Arab Journal of Information Technology , vol. 7, no. 1, pp. 21627, 2010.

[2] Grover L., A Fast Quantum Mechanical Algorithm for Database Search, in Proceedings of 28 th Annual ACM Symposium on the Theory of Computing , USA, pp. 2126221, 1996.

[3] Han K., Genetic Quantum Algorithm and Its Application to Combinatorial Optimization Problem, in Proceedings of IEEE Congress on Evolutionary Computation , USA, pp. 135461360, 2000.

[4] Han K., Park K., Lee C., and Kim J., Parallel Quantum6Inspired Genetic Algorithm for Combinatorial Optimization Problem, in Proceedings of IEEE Congress of Evolutionary Computation , South Korea, pp. 142261429, 2001.

[5] Laboudi Z. and Chikhi S., Evolution d Automates Cellulaires par Algorithmes G n tiques Quantiques, in Proceedings of Conf rence Internationale sur l Informatique et ses Applications , Alg rie, pp. 1611, 2009.

[6] Laboudi Z. and Chikhi S., Evolving Cellular Automata by Parallel Genetic Algorithm, in Proceedings of IEEE Conference on Networked Digital Technologies , Ostrava, pp. 3096314, 2009.

[7] Layeb A. and Saidouni D., Quantum Genetic Algorithm for Binary Decision Diagram Ordering Problem, International Journal of Computer Science and Network Security, vol. 7 no. 9, pp. 1306135, 2007.

[8] Michalewicz Z., Genetic Algorithms+Data Structures=Evolution Programs , Springer6 Verlag, 1999.

[9] Mitchell M., Hraber P., and Crutchfield J., Evolving Cellular Automata to Perform Computation: Mechanisms and Impediments, Journal of Physica D: Lonelier Phenomena , vol. 75, no. 163, pp. 3616391, 1994.

[10] Shor P., Algorithms for Quantum Computation: Discrete Logarithms and Factoring, in Proceedings of the 35 th Annual Symposium on the Foundation of Computer Sciences , NM, pp. 20622, 1994.

[11] Shuxia M. and Weidong J., A New Parallel Quantum Genetic Algorithm with Probability6 Gate and Its Probability Analysis, in Proceedings of International Conference on Intelligent Systems and Knowledge Engineering , pp. 165, 2007. Comparison of Genetic Algorithm and Quantum Genetic Algorithm 249 Zakaria Laboudi is a teacher researcher at Computer Science Department of Larbi Ben M hidi University, Oum El6Bouaghi Algeria. Currently, he is a PhD candidate in complex systems field at Mentouri University of Constantine Algeria. He received his Master s deg ree in computer science in 2009 from Mentouri Universit y of Constantine Algeria. In 2010, he joined the SC AL group of the Laboratory of Complex Systems (MISC) as a member of its researcher team. His current research interests include Complex Systems, Artific ial Life, Parallel and Distributed Computing, Combinatorial Optimization Problems and Meta6 heuristics. Salim Chikhi received his MSc degree in computer systems from University Mentouri Constantine6 Algeria in collaboration with Glasgow University, UK. He received his PhD degree in computer science from University Mentouri Constantine Algeria in 2005. Currently, he is an associate professor at the same University and lead s the SCAL group within the MISC laboratory. His research areas include artificial life and soft com puting techniques applied to complex systems.