title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ²ÐµÑÐµÐ½Ð¸Ñ Ð½ÑÑÐ° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿ÐµÑÐµÐ½Ð¾ÑÐ° Ð·Ð½Ð°Ð½Ð¸Ð¹ Ð¸ ÑÐ¸Ð¼Ð²Ð¾Ð»ÑÐ½Ð¾Ð¹ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a Model for Predicting Flowering Time Chickpeas Using Transfer Learning and Symbolic Regression
Creators: name: Ð¢ÑÑÑÐºÐ¸Ð½, Ð¯ÑÐ¾ÑÐ»Ð°Ð²
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: 2022
Subjects: ÑÐ°Ð·Ð½Ð¾ÑÑÐ½Ð°Ñ ÑÐ²Ð¾Ð»ÑÑÐ¸Ñ
ÑÐ¸Ð¼Ð²Ð¾Ð»ÑÐ½Ð°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÑÑÐ°Ð½ÑÑÐµÑÐ½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
C++
Python
differential evolution
transfer learning
symbolic regression
regression models
genetic programming

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description: The subject of the graduate qualification work is Â«Development of a model forpredicting Chickpea flowering time prediction model using knowledge transfer andsymbolic regressionÂ». Within the framework of this work, a model for predicting the flowering time ofwild chickpeas was developed based on the available data obtained during a genome-wideassociation search, as well as climatic indicators for the period during which the studytook place. The models were selected using a modification of the transfer learningmethod based on instances of the target area. The purpose of this work is to develop a modified transfer learning methodbased on difference evolution and genetic programming in symbolic regression tobuild chickpea bloom prediction models. After the algorithm was implemented, it wasnecessary to apply the obtained models to real data on the genetic factors of the studiedsamples and weather factors at their landing site, analyze the prediction results andcompare these indicators with existing models.In the course of numerical experiments, models were obtained that differ instructure from existing models for predicting the flowering time, but at the same timeare close to them in terms of the quality of predicting the achievement of the growingseason by chickpeas. The software components developed in the course of the work can be used tobuild forecasting models in other subject areas, both related to bioinformatics andhaving no intersections with it. Moreover, the considered combination of methods ofdifference evolution and genetic programming in symbolic regression has proved tobe quite effective, and it makes sense to further look for areas of their application toreal-world problems.Â Â