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AIP conference proceedings

Processes of applying of quantum genetic algorithm in function optimization

Shukhrat Toirov, Ulugbek Narmuradov

Published Jan 1, 2021• 2 citations
Publisher

Abstract

This article describes optimization processes of functions and their solutions using quantum genetic algorithms. These are continuous heuristic optimization methods based on simulated genetic mechanisms, i.e., on dynamic processes in a population such as mutation, crossover, selection, and so on. This process leads to the emergence of a new class called quantum genetic algorithms. In this article, we have presented a discussion of new classes of quantum genetic algorithms, future capabilities, and benefits.

DOI
10.1063/5.0057346

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This article describes optimization processes of functions and their solutions using quantum genetic algorithms. These are continuous heuristic optimization methods based on simulated genetic mechanisms, i.e., on dynamic processes in a population such as mutation, crossover, selection, and so on. This process leads to the emergence of a new class called quantum genetic algorithms. In this article, we have presented a discussion of new classes of quantum genetic algorithms, future capabilities, and benefits.
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