TY - GEN

T1 - Evolutionary Parameter Optimization: A Novel Control Strategy for Chaotic Environments

AU - Мусаев, Андрей

AU - Григорьев, Дмитрий Алексеевич

N1 - Conference code: 12

PY - 2024

Y1 - 2024

N2 - Efficient control of dynamic systems that interact with unstable immersions is of utmost importance across multiple domains, encompassing the stabilization of turbulent flows, generation of signals in radio engineering, and the optimization of asset management in capital markets. The primary challenge lies in the inherent unpredictability of deterministic chaos models, which engenders additional uncertainty. In order to assess the efficacy of control strategies, numerical methods represent the sole viable approach. The study is primarily concerned with the development of empirical algorithms aimed at identifying and forecasting local trends, with the ultimate objective of formulating extrapolation prediction techniques. The investigation centers specifically on speculative trading within currency markets, where stochastic chaos is a prominent characteristic. In contrast to physical and technical problems, currency markets are purely informational and devoid of inertia. Consequently, traditional prediction algorithms reliant on reactive control strategies have proved to be ineffectual. Accordingly, this study endeavors to rectify this efficiency deficiency by exploring control strategies that optimize evolutionary parameters sequentially while approximating the model structure of observation series.

AB - Efficient control of dynamic systems that interact with unstable immersions is of utmost importance across multiple domains, encompassing the stabilization of turbulent flows, generation of signals in radio engineering, and the optimization of asset management in capital markets. The primary challenge lies in the inherent unpredictability of deterministic chaos models, which engenders additional uncertainty. In order to assess the efficacy of control strategies, numerical methods represent the sole viable approach. The study is primarily concerned with the development of empirical algorithms aimed at identifying and forecasting local trends, with the ultimate objective of formulating extrapolation prediction techniques. The investigation centers specifically on speculative trading within currency markets, where stochastic chaos is a prominent characteristic. In contrast to physical and technical problems, currency markets are purely informational and devoid of inertia. Consequently, traditional prediction algorithms reliant on reactive control strategies have proved to be ineffectual. Accordingly, this study endeavors to rectify this efficiency deficiency by exploring control strategies that optimize evolutionary parameters sequentially while approximating the model structure of observation series.

KW - Asset allocation

KW - Evolutionary optimization

KW - Process dynamics prediction

KW - Stationarity

UR - https://www.mendeley.com/catalogue/62580c24-fb76-3c11-93ab-820aae0c3dff/

U2 - 10.1007/978-981-97-0669-3_23

DO - 10.1007/978-981-97-0669-3_23

M3 - Conference contribution

SN - 978-981-97-0668-6

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 243

EP - 251

BT - Computational Data and Social Networks

PB - Springer Nature

CY - Singapore

T2 - The 12th International Conference on Computational Data and Social Networks

Y2 - 11 December 2023 through 13 December 2023

ER -