Modified high convergence genetic algorithm for approximation of molecular potential energy surface | Izvestiya vuzov. Fizika. 2025. № 6. DOI: 10.17223/00213411/68/6/12

Modified high convergence genetic algorithm for approximation of molecular potential energy surface

Modified High Convergence Genetic Algorithm (HCGA) to solve an unregularized, deep parametrized, and non-convex mathematical task is presented. The key feature of HCGA is the partial optimization of an individual's genome using Levenberg-Marquardt algorithm. A model function (MF) is introduced, which is characteristic for the problem of approximating the potential energy surface of molecules. Using MF, the comparative analysis between HCGA, genetic algorithm, Levenberg-Marquardt, and Adam algorithms was conducted.

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Keywords

potential energy surface, machine learning, genetic algorithm, optimization algorithms, iteration algorithms

Authors

Name	Organization	E-mail
Tretyakov Akim K.	Tomsk State University	dr.akim1998@yandex.ru
Kistenev Yury V.	Tomsk State University	yuk@iao.ru

Всего: 2

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