Сравнение эффективности алгоритма «случайный лес» и искусственной нейронной сети класса RNN в задаче управления процессом структурно-параметрического синтеза моделей бизнес-процессов на основе генетического алгоритма
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Comparison of the efficiency of the random forest algorithm and artificial neural networks of the RNN class in the problem of managing the process of structural-parametric synthesis of business process models based on a genetic algorithm

idPetrosov D.A.

UDC 519.7
DOI: 10.26102/2310-6018/2024.47.4.020

  • Abstract
  • List of references
  • About authors

The results presented in this study are relevant for solving the problem of increasing the efficiency of the genetic algorithm in problems related to the use of big data. In the framework of most existing approaches to the application of the evolutionary procedure, efficiency improvement methods are used that are based on classical approaches aimed at pre-setting the operating parameters of the genetic algorithm operators in a specific subject area. At the same time, when working with big data, there is a need to stop and restart the genetic algorithm to obtain the best solutions, since the population of the evolutionary algorithm can be in local extremes and / or the efficiency of the increase in the quality of individuals does not allow finding the required solution in a given time interval. In this case, it becomes relevant to develop new methods that allow you to manage the search process. One of the approaches to solving this problem is the use of the mathematical apparatus of artificial neural networks of the RNN class, which have proven their effectiveness in solving the classification problem and can be used to identify the state of the population of the genetic algorithm. In addition to the approach based on the use of artificial neural networks, it is relevant to assess the possibility of using the "random forest" algorithm to solve the problem of recognizing the state of a population and making decisions on changing the operating parameters of the genetic algorithm operators directly in the process of work, which will allow influencing the trajectory of the population in the solution space. Within the framework of this article, the results of computational experiments on solving the problem of classifying the state of a population of a genetic algorithm by two modern methods will be considered: the "random forest" algorithm and the artificial neural network RNN, the modeling of which is performed using a graph approach based on the theory of Petri nets, which will allow combining the developed models with the model of a genetic algorithm adapted to solving the problem of structural-parametric synthesis using nested Petri nets.

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Petrosov David Aregovich
Candidate of Technical Sciences, assistant professor

ORCID |

Federal State Budgetary Educational Institution of Higher Education Financial University under the Government of the Russian Federation

Moscow, Russian Federation

Keywords: mathematical modeling, business processes, systems analysis, petri net theory, genetic algorithm, artificial neural networks, random forest algorithm

For citation: Petrosov D.A. Comparison of the efficiency of the random forest algorithm and artificial neural networks of the RNN class in the problem of managing the process of structural-parametric synthesis of business process models based on a genetic algorithm. Modeling, Optimization and Information Technology. 2024;12(4). URL: https://moitvivt.ru/ru/journal/pdf?id=1701 DOI: 10.26102/2310-6018/2024.47.4.020 (In Russ).

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Full text in PDF

Received 29.09.2024

Revised 07.11.2024

Accepted 20.11.2024