Quantum Genetic Optimization

Malossini, Andrea and Blanzieri, Enrico and Calarco, Tommaso (2007) Quantum Genetic Optimization. UNSPECIFIED.

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    Abstract

    The complexity of the selection procedure of a genetic algorithm that requires reordering, if we restrict the class of the possible fitness functions to varying fitness functions, is O(N logN) where N is the size of the population. The Quantum Genetic Optimization Algorithm (QGOA) exploits the power of quantum computation in order to speed up genetic procedures. While the quantum and classical genetic algorithms use the same number of generations, the QGOA outperforms the classical one in identifying the high-fitness subpopulation at each generation. In QGOA the classical fitness evaluation and selection procedures are replaced by a single quantum procedure. We show that the complexity of our QGOA is O(1) in terms of number of oracle calls in the selection procedure. Such theoretical results are confirmed by the simulations of the algorithm.

    Item Type: Departmental Technical Report
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA076 Computer software
    Uncontrolled Keywords: Evolutionary computing and genetic algorithms, quantum computation.
    Additional Information: IEEE TRANSACTION ON EVOLUTIONARY COMPUTATION, VOL. 0, NO, 0, MARCH 2007
    Report Number: DIT-07-019
    Repository staff approval on: 07 Jun 2007

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