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.
Highlights
In the late 1980s, genetic algorithms [1] achieved enough popularity as a method of optimization and machine learning
The possibility to emulate a quantum computer has led to a new class of Genetic Algorithms (GAs) known as
The possibility to emulate a quantum computer has led to a new class of GAs, i.e., Quantum Genetic Algorithms” (QGAs)
Summary
In the late 1980s, genetic algorithms [1] achieved enough popularity as a method of optimization and machine learning During this decade the Nobel Prize-winning physicist Richard Feynman thought the possibility of a quantum computer, a computer that operates using the effects of quantum mechanics. Genetic Algorithms (GAs) are search algorithms based on Darwinian natural selection and genetic mechanisms present in organisms [2]. Once a new generation of offspring chromosomes is obtained, the algorithm simulates genetic mechanisms such as crossover and mutation. In the case of crossover this genetic mechanism takes place during the mating between individuals promoting the population convergence towards sub-optimal solutions present in the search space. The possibility to emulate a quantum computer has led to a new class of GAs known as “Quantum Genetic Algorithms”. We present a discussion, future potential, pros and cons of this new class of GAs, including some material presented in a lecture delivered at [11]
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