Evolutionary Parameter Optimization: A Novel Control Strategy for Chaotic Environments

Abstract

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.