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A New Quantum Evolutionary Algorithm with Sifting Strategy for Binary Decision Diagram Ordering Problem

A New Quantum Evolutionary Algorithm with Sifting Strategy for Binary Decision Diagram Ordering Problem

Abdesslem Layeb, Djamel-Eddine Saidouni
Copyright: © 2012 |Pages: 14
ISBN13: 9781466617438|ISBN10: 1466617438|EISBN13: 9781466617445
DOI: 10.4018/978-1-4666-1743-8.ch016
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MLA

Layeb, Abdesslem, and Djamel-Eddine Saidouni. "A New Quantum Evolutionary Algorithm with Sifting Strategy for Binary Decision Diagram Ordering Problem." Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications, edited by Yingxu Wang, IGI Global, 2012, pp. 220-233. https://doi.org/10.4018/978-1-4666-1743-8.ch016

APA

Layeb, A. & Saidouni, D. (2012). A New Quantum Evolutionary Algorithm with Sifting Strategy for Binary Decision Diagram Ordering Problem. In Y. Wang (Ed.), Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications (pp. 220-233). IGI Global. https://doi.org/10.4018/978-1-4666-1743-8.ch016

Chicago

Layeb, Abdesslem, and Djamel-Eddine Saidouni. "A New Quantum Evolutionary Algorithm with Sifting Strategy for Binary Decision Diagram Ordering Problem." In Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications, edited by Yingxu Wang, 220-233. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1743-8.ch016

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Abstract

In this work, the authors focus on the quantum evolutionary quantum hybridization and its contribution in solving the binary decision diagram ordering problem. Therefore, a problem formulation in terms of quantum representation and evolutionary dynamic borrowing quantum operators are defined. The sifting search strategy is used in order to increase the efficiency of the exploration process, while experiments on a wide range of data sets show the effectiveness of the proposed framework and its ability to achieve good quality solutions. The proposed approach is distinguished by a reduced population size and a reasonable number of iterations to find the best order, thanks to the principles of quantum computing and to the sifting strategy.

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