title: ÐÐ¸Ð¿ÐµÑÑÐ²ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð¿Ð¾Ð´Ñ Ð¾Ð´ Ðº Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð² Ð±Ð¸Ð¾Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐµ
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: Hyperheuristic Approach to Model Parameterization in Bioinformatics
Creators: name: Ð¡Ð°Ð»Ð¸ÑÐ¾Ð², ÐÐ°Ð³Ð¾Ð¼ÐµÑ
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: Ð³Ð¸Ð¿ÐµÑÑÐ²ÑÐ¸ÑÑÐ¸ÐºÐ°
ÑÐ°Ð·Ð½Ð¾ÑÑÐ½Ð°Ñ ÑÐ²Ð¾Ð»ÑÑÐ¸Ñ
Python
sine-cosine algorithm
rastrigin
DEEP
SCA
EvoHyp
hyperheuristics
differential evolution
deepmethod

description: ÐÐ°Ð½Ð½Ð°Ñ Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÑÐºÐ°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð³Ð¸Ð¿ÐµÑÑÐ²ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ Ð±Ð¸Ð¾Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐ¸. ÐÐ°Ð´Ð°ÑÐ¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1) ÐÐ·ÑÑÐ¸ÑÑ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ Ð²Ð¸Ð´Ñ ÑÐ²ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¸ ÑÐ¿Ð¾ÑÐ¾Ð±Ñ Ð¸Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ. 2) Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð³Ð¸Ð¿ÐµÑÑÐ²ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð² Ð±Ð¸Ð¾Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐµ. 3) Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð½Ð¸Ð·ÐºÐ¾ÑÑÐ¾Ð²Ð½ÐµÐ²ÑÐµ ÑÐ²ÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð´Ð»Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð² Ð³Ð¸Ð¿ÐµÑÑÐ²ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ðµ. 4) ÐÑÐ¾Ð²ÐµÑÑÐ¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð½Ð° Ð·Ð°Ð´Ð°ÑÐµ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ Ð¿ÑÐ¾Ð²ÐµÑÑÐ¸ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð². Ð¡ÑÑÐµÑÑÐ²ÑÑÑÐ¸Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾ÑÐ»ÑÐ¶Ð¸Ð»Ð¸ Ð¾ÑÐ½Ð¾Ð²Ð¾Ð¹ Ð´Ð»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð³Ð¸Ð¿ÐµÑÑÐ²ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð°, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð±ÑÐ» ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ°ÑÐ¸Ñ EvoHyp-python. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ ÑÐ²ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð±ÑÐ»Ð¸ Ð²ÑÐ±ÑÐ°Ð½Ñ Sine-Cosine Ð¼ÐµÑÐ¾Ð´, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¼ÐµÑÐ¾Ð´ ÑÐ°Ð·Ð½Ð¾ÑÑÐ½Ð¾Ð¹ ÑÐ²Ð¾Ð»ÑÑÐ¸Ð¸ DEEP. ÐÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ñ ÑÐµÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð½Ð° ÑÑÐ½ÐºÑÐ¸ÑÑ Ð Ð°ÑÑÑÐ¸Ð³Ð¸Ð½Ð°, Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¸ÑÐ»ÐµÐ½Ð½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿Ð¾Ð´ÑÐ²ÐµÑÐ´Ð¸Ð» ÐµÐ³Ð¾ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¸ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ñ Ð² ÑÐµÑÐµÐ½Ð¸Ð¸ Ð·Ð°Ð´Ð°Ñ Ð±Ð¸Ð¾Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐ¸.
description: This bachelors thesis is dedicated to the development of a hyperheuristic algorithm for solving bioinformatics problems. Research objectives: 1) Study various types of heuristic algorithms and methods of their creation. 2) Develop a hyperheuristic algorithm for solving the parametrization problem in bioinformatics. 3) Develop low-level heuristics for use in the hyperheuristic algorithm. 4) Test the developed method on the task of optimizing model parameters and conduct a comparative analysis of the results. Existing research served as the basis for the development of the hyperheuristic method, which was implemented using the EvoHyp-python toolkit. The Sine-Cosine method and the Differential Evolution DEEP method were chosen as heuristic optimization algorithms. Tests of the developed algorithm were conducted on Rastrigin functions, and the analysis of numerical results confirmed its effectiveness and prospects in solving bioinformatics problems.