Innovative Evolutionary Computing for Glottochronology and Linguistic Evolution: Trends, Paradigms, and Open Challenges

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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 evolutionary linguistics and glottochronology. It explores the potential for the algorithmic reconstruction of linguistic relatedness, cognate analysis, the construction of phylogenetic models, and the simulation of linguistic changes over time. The study substantiates the promise of hybrid, multi -objective, adaptive, and robust evolutionary methods in the face of the turbulence, uncertainty, and systemic risks characterizing the modern linguistic environment.

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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

Philology and Linguistics
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 Evolutionary Computing for Glottochronology and Linguistic Evolution: Trends, Paradigms, and Open Challenges. Scientific Collection «InterConf», 311, 100–109. https://interconf.openpubarchive.com/index.php/proceeding/article/view/113

References

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

Goncharenko S. Intelligent technologies for modern sociolinguistics (2025). Science: Development and Factors its Influence: Proceedings of the 6th International Scientific and Practical Conference (October 6-8, 2025; Amsterdam, Netherlands). - Amsterdam: Scientific Collection «InterConf», 2025. - pp. 61-64.

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

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

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

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

Goncharenko S. (2025). BIG Data in modern literary studies. Science: Development and Factors its Influence: Proceedings of the 6th International Scientific and Practical Conference (October 6-8, 2025; Amsterdam, Netherlands). - Amsterdam: Scientific Collection «InterConf», 2025. - P. 82-85.

Goncharenko S. (2025). Big semi -structured data & deep ANN in computational linguistics. Science and Global Challenges in the Modern World: Proceedings of the 2nd International Scientific Conference (Leicester, United Kingdom, 5 October 2025). - Lulu Press, Inc., 2025. - pp. 133-136.

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].

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

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

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