Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

Wang, Huaixiao; Li, Ling; Liu, Jianyong; Wang, Yong; Fu, Chenglong

doi:10.1155/2014/164961

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Quantum Computing and Quantum Evolutionary Algorithms1

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2016

2022

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Cited by 3 publications

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“…All kinds of quantum evolutionary algorithms have been successfully applied to optimization problems, such as the personnel scheduling problem [42], dynamic economic dispatch problem, e.g., in power systems [43], multi-sensor image registration [44], cryptanalysis [45], etc.…”

Section: Quantum Computing and Quantum Evolutionary Algorithmsmentioning

confidence: 99%

Quantum Genetic Algorithms for Computer Scientists

Lahoz-Beltrá

2016

Computers

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Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as "Quantum Genetic Algorithms" (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs "avoiding" the possible difficulties of quantum-mechanical phenomena.

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