Next Article in Journal
Development and Evaluation of the Virtual Prototype of the First Saudi Arabian-Designed Car
Previous Article in Journal
Non-Invasive Sensor Technology for the Development of a Dairy Cattle Health Monitoring System
Article Menu

Export Article

Open AccessReview
Computers 2016, 5(4), 24;

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
Full-Text   |   PDF [11230 KB, uploaded 15 October 2016]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Computers EISSN 2073-431X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top