Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments
We consider the problem of evolutionary self-organization of control strategies using the example of speculative trading in a non-stationary immersion market environment. The main issue that obstructs obtaining real profit is the extremely high instability of the system component of observation seri...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/11/1797 |
| _version_ | 1797492697598001152 |
|---|---|
| author | Alexander Musaev Andrey Makshanov Dmitry Grigoriev |
| author_facet | Alexander Musaev Andrey Makshanov Dmitry Grigoriev |
| author_sort | Alexander Musaev |
| collection | DOAJ |
| description | We consider the problem of evolutionary self-organization of control strategies using the example of speculative trading in a non-stationary immersion market environment. The main issue that obstructs obtaining real profit is the extremely high instability of the system component of observation series which implement stochastic chaos. In these conditions, traditional techniques for increasing the stability of control strategies are ineffective. In particular, the use of adaptive computational schemes is difficult due to the high volatility and non-stationarity of observation series. That leads to significant statistical errors of both kinds in the generated control decisions. An alternative approach based on the use of dynamic robustification technologies significantly reduces the effectiveness of the decisions. In the current work, we propose a method based on evolutionary modeling, which supplies structural and parametric self-organization of the control model. |
| first_indexed | 2024-03-10T01:07:25Z |
| format | Article |
| id | doaj.art-6e7ffbdfaf024290b80bb341e25618d4 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:07:25Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-6e7ffbdfaf024290b80bb341e25618d42023-11-23T14:24:40ZengMDPI AGMathematics2227-73902022-05-011011179710.3390/math10111797Evolutionary Optimization of Control Strategies for Non-Stationary Immersion EnvironmentsAlexander Musaev0Andrey Makshanov1Dmitry Grigoriev2St. Petersburg State Technological Institute (Technical University), 190013 St. Petersburg, RussiaDepartment of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping, 198035 St. Petersburg, RussiaCenter of Econometrics and Business Analytics (CEBA), St. Petersburg State University, 199034 St. Petersburg, RussiaWe consider the problem of evolutionary self-organization of control strategies using the example of speculative trading in a non-stationary immersion market environment. The main issue that obstructs obtaining real profit is the extremely high instability of the system component of observation series which implement stochastic chaos. In these conditions, traditional techniques for increasing the stability of control strategies are ineffective. In particular, the use of adaptive computational schemes is difficult due to the high volatility and non-stationarity of observation series. That leads to significant statistical errors of both kinds in the generated control decisions. An alternative approach based on the use of dynamic robustification technologies significantly reduces the effectiveness of the decisions. In the current work, we propose a method based on evolutionary modeling, which supplies structural and parametric self-organization of the control model.https://www.mdpi.com/2227-7390/10/11/1797chaotic processescontrol strategiesnon-stationary environmentchannel strategiesobservation seriesnumerical studies |
| spellingShingle | Alexander Musaev Andrey Makshanov Dmitry Grigoriev Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments Mathematics chaotic processes control strategies non-stationary environment channel strategies observation series numerical studies |
| title | Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments |
| title_full | Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments |
| title_fullStr | Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments |
| title_full_unstemmed | Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments |
| title_short | Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments |
| title_sort | evolutionary optimization of control strategies for non stationary immersion environments |
| topic | chaotic processes control strategies non-stationary environment channel strategies observation series numerical studies |
| url | https://www.mdpi.com/2227-7390/10/11/1797 |
| work_keys_str_mv | AT alexandermusaev evolutionaryoptimizationofcontrolstrategiesfornonstationaryimmersionenvironments AT andreymakshanov evolutionaryoptimizationofcontrolstrategiesfornonstationaryimmersionenvironments AT dmitrygrigoriev evolutionaryoptimizationofcontrolstrategiesfornonstationaryimmersionenvironments |