量子遗传算法的进展、应用与展望

L. Shuguang, Ba Lin
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引用次数: 1

摘要

量子遗传算法(QGA)是量子计算与遗传算法相结合的产物。该算法具有全局优化能力强、收敛速度快、种群规模小等优点。介绍了量子遗传算法(QGA)的原理、方法和基本流程,综述了近年来量子遗传算法的研究进展,包括理论基础的编码扩展、算子的创新、量子门的旋转角度、复杂高维函数的优化、混合算法等,并对QGA的应用现状进行了讨论。最后,提出了QGA未来的发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Progress, Application and Prospect of Quantum Genetic Algorithm
The quantum genetic algorithm (QGA) is derived from the integration of quantum computation and genetic algorithm. It is characterized with advantages like strong global optimization ability, fast convergence speed and small population size. In this paper, the principle, method and basic flow of quantum genetic algorithm (QGA) are introduced, and the research progress of QGA in recent years is reviewed, including the coding extension of theoretical basis, the innovation of operators, the rotation angle of quantum gates, the optimization of complex high-dimensional functions, and hybrid algorithms, and then he application status of QGA is also discussed. Finally, the future development direction of QGA is put forward.
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