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Quantum Behaved Genetic Algorithm: Constraints-Handling and GPU Computing

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Intelligent Systems in Science and Information 2014 (SAI 2014)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 591))

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Abstract

Quantum-inspired evolutionary algorithm is a new evolutionary algorithm using concepts and principles of quantum computing to work on classical computer rather than quantum mechanical hardware. This article introduces main concepts behind the intersection between evolutionary algorithms and quantum computing, such as quantum-bit, superposition feature, quantum gate, quantum measurement and quantum interference. These behaviors of quantum concepts offer computational power and computational intelligence that must be harnessed and used. Intelligence is the main focus to design novel constraint-handling technique with quantum behaved genetic algorithm (QBGA) to solve well known constrained benchmark problems. Single quantum chromosome represents multiple solutions at the same time, so the same infeasible solutions based on quantum features are also feasible ones. Finally GPU (Graphics Processing Unit) will be discussed with (QBGA) to achieve parallel processing and speed up execution time, especially to solve high dimensional real world optimization problems requiring intensive computing resources.

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Author information

Authors and Affiliations

  1. Department of Operations Research, Menofia University, Menofia, Egypt

    Amgad M. Mohammed & N. A. Elhefnawy

  2. Department of Operations Research, Institute of Statistical Studies and Research, Giza, Egypt

    Mahmoud M. El-Sherbiny

  3. Department of Information Technology, Menofia University, Menofia, Egypt

    Mohiy M. Hadhoud

Authors
  1. Amgad M. Mohammed
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  2. N. A. Elhefnawy
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  3. Mahmoud M. El-Sherbiny
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  4. Mohiy M. Hadhoud
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Corresponding author

Correspondence to Amgad M. Mohammed .

Editor information

Editors and Affiliations

  1. Graduate School of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, London, United Kingdom

    Rahul Bhatia

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© 2015 Springer International Publishing Switzerland

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Mohammed, A.M., Elhefnawy, N.A., El-Sherbiny, M.M., Hadhoud, M.M. (2015). Quantum Behaved Genetic Algorithm: Constraints-Handling and GPU Computing. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-14654-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14653-9

  • Online ISBN: 978-3-319-14654-6

  • eBook Packages: EngineeringEngineering (R0)

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