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

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Published: 26.09.2026

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Svitlana Krasniuk
Kyiv National University of Technologies and Design

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.

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Data Availability Statement

All data supporting the findings of this study are presented in the text of the scientific work.

Issue

No. 311 (2026): 17th ISPC «International Forum: Problems and Scientific Solutions» (September 26-28, 2026; Melbourne, Australia).

Section

Literary studies
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Biography

Svitlana Krasniuk, Kyiv National University of Technologies and Design

Senior Lecturer, Department of philology and translation

How to Cite

Krasniuk, S. (2026). Innovative Trends and Prospects of Evolutionary Computation for Complex Authorship Attribution: Navigating Uncertainty and High-Dimensional Linguistic Environments. Scientific Collection «InterConf», 311, 113–122. https://interconf.openpubarchive.com/index.php/proceeding/article/view/115

References

Zhang, Z., Song, W., & Liu, P. (2024). Making and interpreting: Digital humanities as embodied action. Humanities and Social Sciences Communications, 11, Article 13. https://doi.org/10.1057/s41599-023- 02548-3

De Luca, E. W., Fallucchi, F., Ghattas, B., & Spielhaus, R. (2024). The digital transformation processes for supporting digital humanities researchers in text analysis. Journal of Documentation, 80(2), 378–391. https://doi.org/10.1108/JD-07-2022-0143

Goncharenko S. (2025). Methodological bases of innovative information systems in the field of computer linguistics. Trends, Issues, and Challenges in Modern Science: Proceedings of the 2nd International Scientific Conference (Cambridge, United Kingdom, 5 September 2025). - Lulu Press, Inc., 2025. - P. 100 -103. URL: https://er.knutd.edu.ua/handle/123456789/31071

Smits, T., & Wevers, M. (2023). A multimodal turn in Digital Humanities: Using contrastive machine learning models to explore, enrich, and analyze digital visual historical collections. Digital Scholarship in the Humanities, 38 (3), 1267 -1280. https://doi.org/10.1093/llc/fqad008

Rettberg, J. W. (2022). Algorithmic failure as a humanities methodology: Machine learning's mispredictions identify rich cases for qualitative analysis. Big Data & Society, 9 (2). https://doi.org/10.1177/20539517221131290

Grieve, J., Bartl, S., Fuoli, M., Grafmiller, J., Huang, W., Jawerbaum, A., Murakami, A., Perlman, M., Roemling, D., & Winter, B. (2024). The sociolinguistic foundations of language modeling. Frontiers in Artificial Intelligence, 7, 1472411. https://doi.org/10.3389/frai.2024.1472411

Krasnyuk M.T., Naumenko M.A. (2024) Efektyvne zastosuvannia henetychnykh alhorytmiv u vyrishenni bahatoekstremumnykh optymizatsiinykh zadach v menedzhmenti konkurentnoho pidpryiemstva [Effective application of genetic algorithms in solving multi - extremum optimization problems in the management of a competitive enterprise]. Grail of Science, No41, June 2024, pp. 65 -73. URL: https://archive.journal-grail.science/index.php/2710- 3056/issue/view/05.07.2024/29 [in Ukrainian].

Goncharenko S. (2025). Mathematical optimization as element of innovative educational management in modern complex conditions. Science and Global Challenges in the Modern W orld: Proceedings of the 2nd International Scientific Conference (Leicester, United Kingdom, 5 October 2025). - Lulu Press, Inc., 2025. - P. 90-94. URL: https://er.knutd.edu.ua/handle/123456789/31667

Kulynych Y., Krasnyuk M., Krasniuk S. (2022). Efficiency of evolutionary algorithms in solving optimization problems on the example of the fintech industry. Grail of Science, No14-15, May 2022, (pp. 63 -70) DOI: https://doi.org/10.36074/grail-of- science.27.05.2022

Araujo, L. (2009). How evolutionary algorithms are applied to statistical natural language processing. Artificial Intelligence Review, 28(4), 275–303. https://doi.org/10.1007/s10462-009-9104-y

Manzoni, L., Jakobovic, D., Mariot, L., Picek, S., & Castelli, M. (2020). Towards an evolutionary-based approach for natural language processing. arXiv preprint. arXiv:2004.13832. https://arxiv.org/abs/2004.13832

Bungum, L., & Gambäck, B. (2016). Evolutionary Algorithms in Natural Language Processing. In Proceedings of the Workshop on Natural Language Processing and Computational Linguistics. https://www.semanticscholar.org/paper/Evolutionary-Algorithms-in- Natural-Language-Bungum- Gamb%C3%A4ck/d2424a7f83be06b3abd99485ee22f8fd90cb602c