Lahoz Beltrá, Rafael2023-06-182023-06-1820162073-431X10.3390/computers5040024https://hdl.handle.net/20.500.14352/23452Genetic 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.engAtribución 3.0 Españajournal articlehttp://doi.org/10.3390/computers5040024https://www.mdpi.com/2073-431X/5/4/24open access51:57quantum genetic algorithmsquantum evolutionary algorithmsreduced quantum genetic algorithmquantum computingBiomatemáticas2404 Biomatemáticas