title: ÐÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ²ÐµÑÐµÐ½Ð¸Ñ Ð²Ð¸Ð³Ð½Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð½ÑÑ Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹ c ÑÐ°Ð·Ð½Ð¾ÑÑÐ½Ð¾Ð¹ ÑÐ²Ð¾Ð»ÑÑÐ¸ÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Predicting the flowering time of cowpea using approximate Bayesian calculations with difference evolution
Creators: name: ÐÐ³ÐµÐµÐ², ÐÐ½Ð´ÑÐµÐ¹
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: 2021
Subjects: ÐÐ°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ·ÑÐºÐ¸
Ð¾Ð´Ð½Ð¾Ð½ÑÐºÐ»ÐµÐ¾ÑÐ¸Ð´Ð½ÑÐ¹ Ð¿Ð¾Ð»Ð¸Ð¼Ð¾ÑÑÐ¸Ð·Ð¼
single nucleotide polymorphism

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ðº Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ²ÐµÑÐµÐ½Ð¸Ñ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹. Ð¦Ð²ÐµÑÐµÐ½Ð¸Ðµ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ - ÑÐ¸Ð·Ð¸Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ð¹ Ð¿ÑÐ¾ÑÐµÑÑ, Ð¿ÑÐ¾ÑÐµÐºÐ°ÑÑÐ¸Ð¹ Ñ ÑÐ²ÐµÑÐºÐ¾Ð²ÑÑ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹. ÐÐ¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ²ÐµÑÐµÐ½Ð¸Ñ Ð¸Ð³ÑÐ°ÑÑ Ð²Ð°Ð¶Ð½ÑÑ ÑÐ¾Ð»Ñ Ð¿ÑÐ¸ Ð¾ÑÐµÐ½ÐºÐµ ÑÐµÐ»ÑÑÐºÐ¾ÑÐ¾Ð·ÑÐ¹ÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð»Ð° Ð¼Ð½Ð¾Ð³Ð¸Ñ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹. ÐÐ°Ð´ÐµÐ¶Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°ÐºÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ ÑÑÑÐ¾Ð¸ÑÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ñ Ð¿Ð¾ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐ¼ ÑÐ°ÑÑÐµÐ½Ð¸ÑÐ¼ Ð¸ Ð´ÐµÐ»Ð°ÑÑ Ð²ÑÐ²Ð¾Ð´Ñ Ð¾ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÐ¸ Ð²ÑÐ²ÐµÐ´ÐµÐ½Ð¸Ñ ÑÐ°Ð·Ð½ÑÑ ÑÐ¾ÑÑÐ¾Ð² ÑÐ°ÑÑÐµÐ½Ð¸Ð¹. ÐÐ¾Ð´ÐµÐ»Ñ SSM (Simple Simulation Model) - Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ²ÐµÑÐµÐ½Ð¸Ñ Ñ Ð½ÑÑÐ°. Ð Ð°Ð½ÐµÐµ Ð±ÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ SSM, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð²ÐºÐ»ÑÑÐ°ÐµÑ Ð² ÑÐµÐ±Ñ ÑÑÐµÑ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð² Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¸ Ð¿Ð¾Ð´Ð±Ð¾Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÑ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½ÑÑ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐµÐ¹. ÐÐ¾Ð²ÑÐµ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½ÑÐµ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ ÑÑÑÐ¾ÑÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð³ÑÐ°Ð¼Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ²Ð¾Ð»ÑÑÐ¸Ð¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÑÐ¾ÑÐ°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸. ÐÐ¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð±ÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð½Ð° ÑÐ·ÑÐºÐµ C++ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐºÐ¾Ð². Ð¡ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð±ÑÐ»Ð¸ Ð¿Ð¾Ð´Ð¾Ð±ÑÐ°Ð½Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐµ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½ÑÐµ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ Ð´Ð»Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸. SSM Ð²ÐºÐ»ÑÑÐ°ÐµÑ Ð² ÑÐµÐ±Ñ Ð½ÐµÐºÐ¾ÑÐ¾ÑÐ¾Ðµ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð², ÐºÐ¾ÑÐ¾ÑÑÐµ Ð½ÐµÐ¿Ð¾ÑÑÐµÐ´ÑÑÐ²ÐµÐ½Ð½Ð¾ Ð²Ð»Ð¸ÑÑÑ Ð½Ð° ÑÑÑÑÐºÑÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÐ¾Ð»ÐµÐµ Ð½Ð°Ð´ÐµÐ¶Ð½ÑÐ¹ Ð²ÑÐ±Ð¾Ñ ÑÑÐ¸Ñ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¾ÑÑÑÐµÑÑÐ²Ð»ÐµÐ½ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð° Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð½ÑÑ Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð½Ð°Ð·ÑÐ²Ð°ÐµÑÑÑ ABCDE. ÐÐ°Ð½Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð±ÑÐ» ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð½Ð° ÑÐ·ÑÐºÐµ C++ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½ Ðº Ð´Ð°Ð½Ð½ÑÐ¼ Ð¿Ð¾ Ð²Ð¸Ð³Ð½Ðµ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð±ÑÐ»Ð¸ ÑÐ¾Ð±ÑÐ°Ð½Ñ Ð² Ð¢Ð°Ð¹Ð²Ð°Ð½Ðµ, ÐÑÑÑÐ°ÑÐ°Ð½Ð¸ Ð¸ ÐÑÐ±Ð°Ð½Ð¸ Ð·Ð° 1984, 1985, 2017 Ð¸ 2018 Ð³Ð¾Ð´Ð°.
description: In this paper, an approach to optimizing a dynamic model for predicting the time of plant flowering is considered. Flowering of plants is a physiological process that occurs in flowering plants. Dynamic flowering time prediction models play an important role in assessing the agricultural potential of many plants. The reliable results of such models allow making forecasts for various plants and drawing conclusions about the need to breed different plant varieties. The SSM (Simple Simulation Model) is a dynamic model for predicting flowering time for chickpeas. Earlier, a modification of the SSM was developed, which includes the consideration of genetic information in the model and the selection of optimal functional dependencies. New functional dependencies are constructed by the method of grammatical evolution using stochastic optimization. The new model was developed in C ++ using additional frameworks. Optimization was used to find the optimal functional dependencies for the model. SSM includes a number of hyperparameters that directly affect the structure of the model. A more reliable choice of these hyperparameters can be achieved using an approximate Bayesian calculation called ABCDE. This method was implemented in the C ++ language and applied to the data on wigne, which were collected in Taiwan, Astrakhan and Kuban for 1984, 1985, 2017 and 2018.