Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments

Alexander Musaev, Andrey Makshanov and Dmitry Grigoriev
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Alexander Musaev: St. Petersburg State Technological Institute (Technical University), 190013 St. Petersburg, Russia
Andrey Makshanov: Department of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping, 198035 St. Petersburg, Russia
Dmitry Grigoriev: Center of Econometrics and Business Analytics (CEBA), St. Petersburg State University, 199034 St. Petersburg, Russia

Mathematics, 2022, vol. 10, issue 11, 1-17

Abstract: 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.

Keywords: chaotic processes; control strategies; non-stationary environment; channel strategies; observation series; numerical studies; dynamic stability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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