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titleType: Subtitle
title: Development of a model for taking into account genetic and geographical factors for predicting the phenotypic traits of plants using Cartesian genetic programming
Creators: name: ÐÐ¸ÑÐ°Ð»ÑÑÑÐº, ÐÐ°Ð»ÐµÐ½ÑÐ¸Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: cÐ¸Ð¼Ð²Ð¾Ð»ÑÐ½Ð°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð´ÐµÐºÐ°ÑÑÐ¾Ð²Ð¾ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Python
symbolic regression
regression analysis
cartstean genetic programming

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description: Topic of the final qualification work: "Development of a model for taking into account genetic and geographical factors for predicting the phenotypic traits of plants using Cartesian genetic programming." The purpose of this work is to develop a symbolic regression model for predicting the plant phenotype from genetic, geographical and weather data, built using Cartesian genetic programming. Tasks to be solved in the course of this work: development of an algorithm for using and optimizing constants in the Cartesian genetic programming method, development of a regression model for predicting the plant phenotype by genetic, geographical and climatic factors, built using Cartesian genetic programming, studying the influence of various factors on the accuracy of the model solution. In the course of the work, the proposed changes to the Cartesian genetic programming algorithm were implemented, regression models were obtained and studied. The presented work was implemented in Python and C. This development is supposed to be used in bioinformatics, as well as agronomists and farmers.