Innovative Trends and Prospects of Evolutionary Computation for Complex Authorship Attribution: Navigating Uncertainty and High-Dimensional Linguistic Environments

Abstract

The article examines innovative approaches, trends, and prospects regarding the application of evolutionary computation to solve complex problems in authorship attribution and textology. It substantiates the effectiveness of these methods for the intelligent selection and combination of stylometric features, which enable the identification of individual markers of authorial style. Furthermore, the study explores the potential for hybridizing evolutionary algorithms with machine learning and natural language processing technologies amidst turbulence, uncertainty, and systemic risks.