INTELLIGENT DATA ANALYSIS AND HYPERPARAMETER TUNING USING GENETIC ALGORITHMS IN MACHINE LEARNING
Abstract
This research work presents theoretical studies on the mechanisms of machine learning for genetic algorithms and their application in intellectual data analysis. In most fields today, information systems and machine systems are carrying out processes that were previously performed by humans. As a result, while automation is rapidly increasing, it also leads to a rise in unemployment rates. Based on this, improving the population's living conditions and reducing the unemployment rate remain pressing issues. To prevent such situations and improve the standard of living of the population, theoretical research has been conducted on maintaining a balance in unemployment rates through genetic algorithms in the intellectual analysis of data. Based on this, the article presents research on the mechanisms of genetic algorithm technologies for intellectual data analysis and their application. This paper presents the content, mathematical models, and applications of genetic algorithm technologies.
References
1. Axatov A.R., Nurmamatov M.Q., Nazarov F.M. (2022). Mathematical Models of Coordination of Population Employment in the Labor Market Ra journal of applied research[J]. India / – Vol. 8, Issue 2. – Pp. 111–119. https://doi.org/10.47191/rajar/v8i2.09.
2. N. Fayzullo, S. Sariyev and Y. Sherzodjon, "Analyzing the Effectiveness of Ensemble Methods in Solving Multi-Class Classification Problems," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 788-793, doi: 10.1109/SmartIndustryCon65166.2025.10986248
3. John McCall. (2004) Genetic algorithms for modelling and optimisation School of Computing[J], Robert Gordon University, Aberdeen, Scotland, UK Received 27 February 2004; received in revised form 7 July 2004. doi:10.1016/j.cam.2004.07.034.
4. A.Rashidov, D. Mardonov, & A. Soliev. Diagnosis of Diabetes Mellitus Based on Artificial Intelligence Algorithms [C]. 2025 International Russian Smart Industry Conference (SmartIndustryCon) (in press)
5. Akmal Akhatov, Fayzullo Nazarov, Mekhriddin Nurmamatov, Shokhrukh Sariyev. (2024). Genetic algorithm application technology in multi-parameter optimization problems AIP Conf [C]. Proc. 3244, 030025.https://doi.org/10.1063/5.0242074
6. Nurmamatov, M., Kulmirzayeva, Z. “Development of an Intelligent System for Optimization of Employment Information Using Genetic Algorithms” AIP Conference Proceedings, 2024, 3147(1), 040006. doi:10.1063/5.0210279
7. M. Nurmamatov, S. Sariyev and B. Eshonkulov, "Application of Evolutionary Algorithms to Enhance the Efficiency of Neural Networks and Machine Learning Algorithms," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 533-537, doi: 10.1109/SmartIndustryCon65166.2025.10986257
8. Lakhlifa Sadek, Hamad Talibi Alaoui. (2022). Application of MGA and EGA algorithms on large-scale linear systems of ordinary differential equations Journal of Computational ScienceJuly[J] 2022. https://doi.org/10.1016/j.jocs.2022.101719.
9. Nao Hu, Peilin Zhou, Jianguo Yang. (2017). Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation Energy Conversion and Management1 February 2017[J]. http://dx.doi.org/10.1016/j.enconman.2016.11.066.
10. Cantú-P. E. (1988). Survey of parallel genetic algorithms Calculateurs Parallèles Reseaux et Systems Repartis, 10 (2) (1998)[J], pp. 141-171. https://doi.org/10.4236/jsip.2014.53009.
11. Аkhаtоv А.R., Nurmаmаtоv M.Q., Mаrdоnоv D.R., Nаzаrоv F.M. (2021) Imprоvеmеnt оf mаthеmаtiсаl mоdеls оf thе rаting pоint sуstеm оf еmplоуmеnt Sсiеntifiс jоurnаl Sаmаrkаnd stаtе univеrsitу[J]. 2021. – №1(125). –P. 100-107. http://dx.doi.org/10.59251/2181-1296.v1.1251.714.
12. Nurmamatov, M., Kulmirzayeva, Z. (2024). “Development of an Intelligent System for Optimization of Employment Information Using Genetic Algorithms” AIP Conference Proceedings [C], 2024, 3147(1), 040006. https://doi.org/10.1063/5.0210279.
13. Muhuri. P.K, A. Rauniyar. (2017). Immigrants based adaptive genetic algorithms for task allocation in multi-robot systems Int. J. Comput. Intell [J]Appl., 16 (04) (2017), p. 1750025. http://dx.doi.org/10.1142/S1469026817500250.
14. Nazarov, F., Nurmamatov, M., Sariyev, S. (2024). Ma’lumotlarni intellektual tahlil qilish uchun genetik algoritmlar va ularni qo‘llanilishi. digital transformation and artificial intelligence[J], 2(6), 162–168. Retrieved from https://dtai.tsue.uz/index.php/dtai/article/view/v2i630.
