PECULIARITIES OF EVOLUTIONARY MODELING FOR MODERN LITERATURE STUDIES AMID DYNAMIC UNCERTAINTY, TURBULENCE AND SYSTEMIC RISKS: THE SHIVA AND WARMI FRAMEWORKS IN CONTEMPORARY PHILOLOGY

Article Sidebar

PDF
Published: 16.08.2026

Main Article Content

Svitlana Krasniuk
Kyiv National University of Technologies and Design image/svg+xml

Abstract

The article examines the features of the application of evolutionary modeling and evolutionary (genetic) programming in modern literary studies in the SHIVA and WARMI environments. Their capabilities for the analysis of literary texts, stylometry, modeling of literary evolution, and intellectual processing of text data are analyzed, and the prospects for their use in modern digital philology are also identified.

Downloads

Download data is not yet available.

Article Details

Data Availability Statement

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

Issue

No. 305 (2026): 19th ISPC «Scientific Horizon in the Context of Social Crises» (August 16-18, 2026; Tokyo, Japan).

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). PECULIARITIES OF EVOLUTIONARY MODELING FOR MODERN LITERATURE STUDIES AMID DYNAMIC UNCERTAINTY, TURBULENCE AND SYSTEMIC RISKS: THE SHIVA AND WARMI FRAMEWORKS IN CONTEMPORARY PHILOLOGY. Scientific Collection «InterConf», 305, 162–169. https://interconf.openpubarchive.com/index.php/proceeding/article/view/50

References

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; «Scientific Horizon in the Context No Amsterdam, Netherlands). - Amsterdam: Scientific Collection «InterConf», 2025. - P. 82-85.

Goncharenko S. (2025). Intelligent information technologies for innovative management of advanced philology projects. Innovations and New Directions in Scientific Research: Proceedings of the 2nd International Scientific Conference (Manchester, United Kingdom, 20 September 2025). - Lulu Press, Inc., 2025. - P. 176-179. URL: https://er.knutd.edu.ua/handle/123456789/31648

Maxim Krasnyuk, Svitlana Krasniuk (2024). CHAPTER 6. Evolutionary technologies and genetic algorithms in machine translation. Innovation in modern science: Education and Pedagogy, Philosophy, Philology, Art History and Culture, The current stage of development of scientific and technological progress February 2026 Conference proceedings 214 Medicine and Healthcare. Monographic series «European Science». Book 30. Part 3. 2024. pp. 91-98, Published by: ScientificWorld-NetAkhatAV, Lußstr. 1376227 Karlsruhe, Germany https://desymp.promonograph.org/index.php/sge/issue/view/sge30 - 03/sge30-03

Nevmerzhytska S. M. (2018). Formation of a strategy for the innovative development of enterprises in conditions of uncertainty. Scientific Bulletin of the Kherson State University. Series: Economic Sciences. 2018. Vol. 32. pp. 99-103. URL: https://ej.journal.kspu.edu/index.php/ej/article/view/422/418.

Lee, H.-Y., Li, S.-W., & Vu, N. T. (2022). Meta Learning for Natural Language Processing: A Survey. arXiv preprint arXiv:2205.01500. https://arxiv.org/abs/2205.01500

Goncharenko S. (2025). Mathematical optimization as element of innovative educational management in modern complex conditions. 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. - P. 90-94. URL: https://er.knutd.edu.ua/handle/123456789/31667

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, №41, June 2024, 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, №14-15, May 2022, (pp. 63-70). DOI:https://doi.org/10.36074/grail-of- science.27.05.2022

Chang, C. C. (2025). Integrating hybrid AI approaches for enhanced translation quality for low-resource languages. Applied Sciences, 15(16), 9039. https://doi.org/10.3390/app15169039

Costa-Jussà, M. R., & Fonollosa, J. A. R. (2015). Latest trends in hybrid machine translation and its applications. Computer Speech & Language, 32(1), 3-10. https://doi.org/10.1016/j.csl.2014.11.001 168 This work is distributed under the terms of the Creative «Scientific Horizon in the Context No

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

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

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