Implementation of the reconstruction process for constructive-synthesizing models of fractal time series
The presented method focuses on reconstructing constructive models for predefined fractal time series. This work generalizes the approach to implementing the software system that ensures effective automation while considering the specifics of working with model series of various natures– both determ...
Збережено в:
| Дата: | 2026 |
|---|---|
| Автори: | , |
| Формат: | Стаття |
| Мова: | Українська |
| Опубліковано: |
PROBLEMS IN PROGRAMMING
2026
|
| Теми: | |
| Онлайн доступ: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/871 |
| Теги: |
Додати тег
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|
| Назва журналу: | Problems in programming |
| Завантажити файл: |  |
Репозитарії
Problems in programming| Резюме: | The presented method focuses on reconstructing constructive models for predefined fractal time series. This work generalizes the approach to implementing the software system that ensures effective automation while considering the specifics of working with model series of various natures– both deterministic and stochastic. Two main approaches to system implementation based on a genetic algorithm were analyzed: the monolithic and the multi-agent. Considering the complexity of calculating chromosome viability indicators when working with stochastic series, it was decided to separate certain elements of the genetic algorithm– specifically, crossover and mutation– from the selection stage by introducing subpopulations. This made it possible to perform distrib uted computation of chromosome fitness indicators. The introduced solution enabled independent scaling of various elements of the reconstruction process, which increased the overall efficiency of the system. To ensure consistent interaction between elements, different types of communication were considered, among which the asynchronous approach proved to be the most effective. Mechanisms were implemented to optimize interaction between computational entities, as well as a node pattern for implementing crossover and mutation operations. This approach made it possible to eliminate problems associated with processing stochastic time series and en sure controlled and efficient horizontal scaling of the process of reconstructing constructive models.Problems in programming 2025; 4: 3-11 |
|---|