Quantum Genetic Algorithms for Computer Scientists
AbstractGenetic 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Lahoz-Beltra, R. Quantum Genetic Algorithms for Computer Scientists. Computers 2016, 5, 24.
Lahoz-Beltra R. Quantum Genetic Algorithms for Computer Scientists. Computers. 2016; 5(4):24.Chicago/Turabian Style
Lahoz-Beltra, Rafael. 2016. "Quantum Genetic Algorithms for Computer Scientists." Computers 5, no. 4: 24.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.