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Computers 2016, 5(4), 24; doi:10.3390/computers5040024

Quantum Genetic Algorithms for Computer Scientists

Department of Applied Mathematics (Biomathematics), Faculty of Biological Sciences, Complutense University of Madrid, Madrid 28040, Spain
Academic Editor: Prakash Panangaden
Received: 7 July 2016 / Revised: 2 October 2016 / Accepted: 11 October 2016 / Published: 15 October 2016
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Abstract

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. View Full-Text
Keywords: quantum genetic algorithms; quantum evolutionary algorithms; reduced quantum genetic algorithm; quantum computing quantum genetic algorithms; quantum evolutionary algorithms; reduced quantum genetic algorithm; quantum computing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lahoz-Beltra, R. Quantum Genetic Algorithms for Computer Scientists. Computers 2016, 5, 24.

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