Quantum Genetic Algorithms for Computer Scientists

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. Ove...
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Autor*in:	Rafael Lahoz-Beltra [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	quantum genetic algorithms quantum evolutionary algorithms reduced quantum genetic algorithm quantum computing

Übergeordnetes Werk:	In: Computers - MDPI AG, 2013, 5(2016), 4, p 24
Übergeordnetes Werk:	volume:5 ; year:2016 ; number:4, p 24

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DOI / URN:	10.3390/computers5040024

Katalog-ID:	DOAJ032662890

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callnumber-first	Q - Science
author	Rafael Lahoz-Beltra
spellingShingle	Rafael Lahoz-Beltra misc QA75.5-76.95 misc quantum genetic algorithms misc quantum evolutionary algorithms misc reduced quantum genetic algorithm misc quantum computing misc Electronic computers. Computer science Quantum Genetic Algorithms for Computer Scientists
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title	Quantum Genetic Algorithms for Computer Scientists
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title_sort	quantum genetic algorithms for computer scientists
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title_auth	Quantum Genetic Algorithms for Computer Scientists
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.
abstractGer	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.
abstract_unstemmed	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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