Next Article in Journal
Optimizing Hybrid-Channel Supply Chains with Promotional Effort and Differential Product Quality: A Game-Theoretic Analysis
Next Article in Special Issue
Preface to the Special Issue on “Advances in Differential Dynamical Systems with Applications to Economics and Biology”
Previous Article in Journal
A Comprehensive Bibliometric Analysis of Fractional Programming (1965–2020)
Previous Article in Special Issue
Numerical Studies of Channel Management Strategies for Nonstationary Immersion Environments: EURUSD Case Study
 
 
Article

Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments

1
St. Petersburg State Technological Institute (Technical University), 190013 St. Petersburg, Russia
2
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 199178 St. Petersburg, Russia
3
Department of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping, 198035 St. Petersburg, Russia
4
Center of Econometrics and Business Analytics (CEBA), St. Petersburg State University, 199034 St. Petersburg, Russia
*
Author to whom correspondence should be addressed.
Academic Editors: Mihaela Neamțu, Eva Kaslik and Anca Rădulescu
Mathematics 2022, 10(11), 1797; https://doi.org/10.3390/math10111797
Received: 31 March 2022 / Revised: 7 May 2022 / Accepted: 17 May 2022 / Published: 24 May 2022
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. View Full-Text
Keywords: chaotic processes; control strategies; non-stationary environment; channel strategies; observation series; numerical studies; dynamic stability chaotic processes; control strategies; non-stationary environment; channel strategies; observation series; numerical studies; dynamic stability
Show Figures

Figure 1

MDPI and ACS Style

Musaev, A.; Makshanov, A.; Grigoriev, D. Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments. Mathematics 2022, 10, 1797. https://doi.org/10.3390/math10111797

AMA Style

Musaev A, Makshanov A, Grigoriev D. Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments. Mathematics. 2022; 10(11):1797. https://doi.org/10.3390/math10111797

Chicago/Turabian Style

Musaev, Alexander, Andrey Makshanov, and Dmitry Grigoriev. 2022. "Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments" Mathematics 10, no. 11: 1797. https://doi.org/10.3390/math10111797

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop