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Practicality of training a quantum-classical machine in the noisy intermediate-scale quantum era. [PDF]

iScience
Dutta T, Jin A, Huihong CL, Latorre JI, Mukherjee M.   +4 more
europepmc   +1 more source

Quantum genetic selection [PDF]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021
This paper proposes an innovative selection operator based on concepts from quantum mechanics. In particular, a quantum state is used to embody genetic individuals and their fitness values, and a quantum algorithm known as amplitude amplification is used to modify this state in order to create a quantum superposition in which the probability to measure
Acampora G., Schiattarella R., Vitiello A.   +2 more
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Sexual Selection for Genetic Algorithms

Artificial Intelligence Review, 2003
Genetic Algorithms (GA) have been widely used in operations research and optimization since first proposed. A typical GA comprises three stages, the encoding, the selection and the recombination stages. In this work, we focus our attention on the selection stage of GA, and review a few commonly employed selection schemes and their associated scaling ...
GOH, Kai Song, LIM, Andrew, Rodrigues, Brian   +2 more
openaire   +3 more sources

On the effect of selection in genetic algorithms

Random Structures and Algorithms, 2001
Summary: To study the effect of selection with respect to mutation and mating in genetic algorithms, we consider two simplified examples in the infinite population limit. Both algorithms are modeled as measure valued dynamical systems and are designed to maximize a linear fitness on the half line. Thus, they both trivially converge to infinity.
Christian Mazza, Didier Piau
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A genetic algorithm with disruptive selection

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1996
Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. Applying the "survival-of-the-fittest" principle, traditional genetic algorithms allocate more trials to above-average schemata.
Ting Kuo, Shu-Yuen Hwang
openaire   +3 more sources