Quantum Genetic Optimization

Malossini, A.; Blanzieri, Enrico; Calarco, Tommaso

doi:10.1109/tevc.2007.905006

Cited by 114 publications

(83 citation statements)

References 16 publications

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“…In 2008 [63], a similar idea is also applied to other version of a true quantum evolutionary algorithm which was termed as Quantum Genetic Optimization Algorithm (QGOA).…”

Section: Towards True Quantum Evolutionary Algorithmsmentioning

confidence: 99%

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Quantum Genetic Algorithms for Computer Scientists

Lahoz-Beltrá

2016

Computers

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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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“…In 2008 [63], a similar idea is also applied to other version of a true quantum evolutionary algorithm which was termed as Quantum Genetic Optimization Algorithm (QGOA).…”

Section: Towards True Quantum Evolutionary Algorithmsmentioning

confidence: 99%

“…In 2008 [63], a similar idea is also applied to other version of a true quantum evolutionary algorithm which was termed as Quantum Genetic Optimization Algorithm (QGOA). In a second step the algorithm searches for the maximum fitness: In a second step the algorithm searches for the maximum fitness:…”

Section: Towards True Quantum Evolutionary Algorithmsmentioning

confidence: 99%

Quantum Genetic Algorithms for Computer Scientists

Lahoz-Beltrá

2016

Computers

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“…Udrescu et al propose RQGA [62], which provides a mechanism to overcome this problem. Hybrid versions [63] merge QGA with permutation-based GA and [64] merge QGA with real-valued GA. Malossini and Calarco propose QGOA [65] very similar to QGA with special quantum-based selection and fitness evaluation methods.…”

Section: Quantum-mechanics-based Algorithmsmentioning

confidence: 99%

Physics-Inspired Optimization Algorithms: A Survey

Biswas

Mishra

Tiwari

et al. 2013

Journal of Optimization

109

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Natural phenomenon can be used to solve complex optimization problems with its excellent facts, functions, and phenomenon. In this paper, a survey on physics-based algorithm is done to show how these inspirations led to the solution of well-known optimization problem. The survey is focused on inspirations that are originated from physics, their formulation into solutions, and their evolution with time. Comparative studies of these noble algorithms along with their variety of applications have been done throughout this paper.

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“…Attempts to bring quantum computing power to a greater range of problems are the development of quantum machine-learning algorithms as decision trees [6], evolutionary algorithms [7] and artificial neural networks [8,9,10,11,12,13,14,15,16]. In this paper, we are concerned in the field of quantum weightless neural networks.…”

Section: Introductionmentioning

confidence: 99%

Weightless neural network parameters and architecture selection in a quantum computer

2016

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Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture. To avoid this empirical process several techniques have been proposed to automatise the architecture selection process. In this paper, we propose a method to perform parameter and architecture selection for a quantum weightless neural network (qWNN). The architecture selection is performed through the learning procedure of a qWNN with a learning algorithm that uses the principle of quantum superposition and a non-linear quantum operator. The main advantage of the proposed method is that it performs a global search in the space of qWNN architecture and parameters rather than a local search.

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Quantum Genetic Optimization

Cited by 114 publications

References 16 publications

Quantum Genetic Algorithms for Computer Scientists

Quantum Genetic Algorithms for Computer Scientists

Physics-Inspired Optimization Algorithms: A Survey

Weightless neural network parameters and architecture selection in a quantum computer

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