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Quantum inspired evolutionary algorithms are heuristic search methods, where all individuals in the search space directed to the best solution position. Using quantum gate operator with other evolutionary operators such as selection, crossover and mutation constitute a challenge in terms of their types and their parameters. In this paper we design several quantum crossover and quantum mutation operators...
Quantum computing proved good results and performance when applied to solving optimization problems. This paper proposes a quantum crossover-based quantum genetic algorithm (QXQGA) for solving non-linear programming. Due to the significant role of mutation function on the QXQGA's quality, a number of quantum crossover and quantum mutation operators are presented for improving the capabilities of searching,...
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