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Convergence analysis of quantum-inspired genetic algorithms with the population of a single individual

Published: 12 July 2008 Publication History

Abstract

In this paper, the Quantum-inspired Genetic Algorithms with the population of a single individual are formalized by a Markov chain model using a single and the stored best individual. Here, we analyze the convergence property of the Quantum-inspired Genetic Algorithms based on our proposed mathematical model, and with assumption in which its special genetic operation in the generation changes is restricted to a quantum operator; and show by means of the Markov chain analysis that the algorithm with preservation of the best individual in the population and comparison of it with the existing individual, will converge on the global optimal solution.

References

[1]
Han, K.-H., and Kim, J.-H. Genetic Quantum Algorithm and its Application to Combinatorial Optimization Problem. In Proceeding of the 2000 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, vol. 2, July 2000, 1354--1360.
[2]
Han, K.-H., and Kim, J.-H. On the Analysis of the Quantum-inspired Evolutionary Algorithm with a Single Individual. Proceedings of the 2006 IEEE Congress on Evolutionary Computation, IEEE Press, 9172--9179, July 2006.
[3]
Feller, W. An Introduction to Probability Theory and its Applications, volume 1. 3rd edition, New York: John Wiley and Sons, 1968.
[4]
Rudolph, G. Convergence Analysis of Canonical Genetic Algorithms. IEEE Transactions on Neural Network, vol. 5, no. 1, 1994, 96--101.

Cited By

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  • (2022)A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor systemApplied Soft Computing10.1016/j.asoc.2022.109807131(109807)Online publication date: Dec-2022
  • (2017)Success rates analysis of three hybrid algorithms on SAT instancesSwarm and Evolutionary Computation10.1016/j.swevo.2017.02.00134(119-129)Online publication date: Jun-2017
  • (2011)Convergence analysis on a class of quantum-inspired evolutionary algorithms2011 Seventh International Conference on Natural Computation10.1109/ICNC.2011.6022161(1072-1076)Online publication date: Jul-2011
  • Show More Cited By

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Published In

cover image ACM Conferences
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
July 2008
1814 pages
ISBN:9781605581309
DOI:10.1145/1389095
  • Conference Chair:
  • Conor Ryan,
  • Editor:
  • Maarten Keijzer
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 12 July 2008

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Author Tags

  1. global convergence
  2. markov chain model
  3. quantum-inspired genetic algorithm

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Cited By

View all
  • (2022)A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor systemApplied Soft Computing10.1016/j.asoc.2022.109807131(109807)Online publication date: Dec-2022
  • (2017)Success rates analysis of three hybrid algorithms on SAT instancesSwarm and Evolutionary Computation10.1016/j.swevo.2017.02.00134(119-129)Online publication date: Jun-2017
  • (2011)Convergence analysis on a class of quantum-inspired evolutionary algorithms2011 Seventh International Conference on Natural Computation10.1109/ICNC.2011.6022161(1072-1076)Online publication date: Jul-2011
  • (2010)Markov chain models for genetic algorithm based topology control in MANETsProceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II10.1007/978-3-642-12242-2_5(41-50)Online publication date: 7-Apr-2010

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