title: Ð ÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ñ ÑÐµÐ½Ð¾ÑÐ¸Ð¿Ð° ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ
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: Regression model for describing plant phenotype using genetic programming
Creators: name: Ð¯ÐºÑÐ±Ð¾Ð², ÐÐ°ÑÐº
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: 2024
Subjects: ÑÐ¸Ð¼Ð²Ð¾Ð»ÑÐ½Ð°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð°Ð½ÑÐ°Ð¼Ð±Ð»Ð¸ Ð´ÐµÑÐµÐ²ÑÐµÐ²
Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð³ÑÐ°Ð¼Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐ²Ð¾Ð»ÑÑÐ¸Ñ
python
symbolic regression
regression models
trees ensembles
genetic programming
grammatical evolution

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description: The subject of the graduate qualification work is Â«A regression model for describing plant phenotype using genetic programmingÂ». In the course of this work, methods of model building based on ensembles of grammatical evolution algorithms for predicting phenotypic traits of rye from genotypic marker data were developed. The objectives of this paper are to develop and compare ensemble-based symbolic regression methods based on difference evolution and genetic programming for model building prediction of rye phenotypic traits. After implementation, the obtained models were applied to real data of genetic factors of rye samples. Numerical results were obtained, indicating the validity of gradient boosting as an approach to combining genetic programming models. The random forest approach, perhaps due to the specificity of the data and the problem as a whole, failed to show decent results. The importance of genotypic markers was then assessed using the methods of Shapley values and residual sum of squares. The developed models can also be used for problems of other sciences, where approximate restoration of functional dependence in explicit, symbolic form is required. The more so that the ensemble of genetic programming models in symbolic regression proved to be an effective modification for improving the predictive accuracy of genetic algorithms, which may be in demand in other fields as well.