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Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia, 2024, Volume 520, Number 2, Pages 30–40
DOI: https://doi.org/10.31857/S2686954324700358 (Mi danma585) 		 
SPECIAL ISSUE: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES

Machine search problem for mathematical expression

A. I. Diveev, E. A. Sofronova

Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
DOI: https://doi.org/10.31857/S2686954324700358
Abstract: The problem of searching for a mathematical expression is formulated. Classes of mathematical problems where this problem is in demand are presented. A general approach to solving this problem based on machine learning using symbolic regression methods is given. This approach allows you to find not only the parameters of the desired mathematical expression, but also its structure. The general problem of machine learning by methods of symbolic regression is described and a unified approach to overcoming it based on the application of the principle of small variations of the basis solution is given.
Keywords: symbolic regression, non-numerical optimization evolutionary computation, genetic algorithm, genetic programming, network operator method, cartesian genetic programming.
Received: 30.09.2024
Accepted: 02.10.2024
English version:
Doklady Mathematics, 2024, Volume 110, Issue suppl. 1, Pages S25–S34
DOI: https://doi.org/10.1134/S1064562424602087
Bibliographic databases:
Document Type: Article
UDC: 519.6
Language: Russian
Citation: A. I. Diveev, E. A. Sofronova, “Machine search problem for mathematical expression”, Dokl. RAN. Math. Inf. Proc. Upr., 520:2 (2024), 30–40; Dokl. Math., 110:suppl. 1 (2024), S25–S34
Citation in format AMSBIB
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\by A.~I.~Diveev, E.~A.~Sofronova
\paper Machine search problem for mathematical expression
\jour Dokl. RAN. Math. Inf. Proc. Upr.
\yr 2024
\vol 520
\issue 2
\pages 30--40
\mathnet{http://mi.mathnet.ru/danma585}
\elib{https://elibrary.ru/item.asp?id=80287433}
\transl
\jour Dokl. Math.
\yr 2024
\vol 110
\issue suppl. 1
\pages S25--S34
\crossref{https://doi.org/10.1134/S1064562424602087}
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