Evolutionary Search for Stochastic Optimization with Binary Choice Relations Under Fuzzy Modeling

Abstract

The article is devoted to presenting an approach to decision-making in fuzzy modeling of systems based on a limited number of experiments characterizing the system’s behavior. An iterative algorithm is proposed for use, in which the functions of generation and selection of solutions with several branches of evolutionary search are successively implemented. The generation function is built, for the most part, regardless of the content of the task. The selection function is built using a selection procedure that is completely dependent on the problem to be solved. The resulting information is used to guide the search process, making it understandable for guided machine learning. The convergence of algorithms for finding optimal solutions in the presence of constraints in the form of inequalities and additional constraints in the form of binary relations is analyzed. The results of solving test problems of stochastic optimization are given. The described approach solves the problem of fuzzy modeling for decision-making based on a limited set of experimental data, which makes it possible to identify regularities and generalize them to evaluate the performance and accuracy of machine learning algorithms.

Received: 21 February 2024 | Revised: 1 April 2024 | Accepted: 29 April 2024

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study