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The Effectiveness of Genetic Algorithms For Evaluating E-Business Strategies
Mshvidobadze T. I., Osadze L. T., Sosanidze M. O.

Mshvidobadze, Tinatin Ia., Osadze, Lali T., and Sosanidze, Maka O. (2025) “The Effectiveness of Genetic Algorithms For Evaluating E-Business Strategies.” Business Inform 10:213–220.
https://doi.org/10.32983/2222-4459-2025-10-213-220

Section: Information Technologies in the Economy

Article is written in English
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UDC 004.9

Abstract:
Nowadays, timely transformation of information is important for the viability of an organization. Big data solutions directly affect how an organization should work with the help of artificial intelligence components. The article shows an algorithmic approach to strategic planning and performance assessment of e-business. Various artificial intelligence methodologies and their use in various applications of large organizations are shown. The conception of genetic algorithms is presented, which is related to e-business strategy in various applications. Genetic algorithms can be used to solve e-business problems, especially for strategic planning and performance evaluation, leading to improved overall performance of large organizations. A new scheme for e-business strategy planning and performance evaluation, based on adaptive algorithmic modeling techniques, is used to improve the performance of genetic algorithms. The proposed algorithmic approach can be effectively used to solve a wide class of e-business and strategic management problems. In the context of “Genetic Algorithm Optimization, Genetic Algorithm Optimization of Business Strategies”, we can delve into the future trends of genetic algorithms optimization for business strategies: 1) The development and improvement of genetic algorithms is expected to lead to improved performance in business strategy optimization. This can be achieved through more efficient selection mechanisms, crossover techniques, and mutation operators; 2) The integration of genetic algorithms with machine learning techniques holds great potential for business strategy optimization. By combining the power of genetic algorithms with the ability to learn from data, businesses can uncover hidden patterns and make more informed decisions; 3) Genetic algorithms are well suited to solving multi-objective optimization problems, where multiple conflicting objectives must be considered simultaneously. Future trends may focus on developing advanced techniques to effectively handle such complex scenarios; 4) As technology advances, genetic algorithms can be used in real-time scenarios, allowing businesses to optimize their strategies on the fly. This can be especially useful in dynamic environments where rapid adaptation is crucial; 5) Genetic algorithms can be combined with other optimization techniques, such as simulated annealing or particle swarm optimization, to create hybrid approaches. These hybrid methods can leverage the strengths of different algorithms and provide more robust optimization solutions.

Keywords: e-business, strategic planning, artificial intelligence, genetic algorithm, performance evaluation.

Fig.: 1. Bibl.: 16.

Mshvidobadze Tinatin Ia. – Doctor of Sciences (Engineering), Professor, Gori State Pedagogical University (53 Ilia Chavchavadze Ave., Gori, 1400, Georgia)
Email: [email protected]
Osadze Lali T. – Doctor of Sciences (Economics), Professor, Gori State Pedagogical University (53 Ilia Chavchavadze Ave., Gori, 1400, Georgia)
Email: [email protected]
Sosanidze Maka O. – Professor, Gori State Pedagogical University (53 Ilia Chavchavadze Ave., Gori, 1400, Georgia)
Email: [email protected]

List of references in article

Coltman, T., Devinney, M., Midgley, F., & Venaik, S. (2008). Formative versus reflective measurement models: Two applications of formative measurement. Journal of Business Research, 61(12), 1250–1262. https://doi.org/10.1016/j.jbusres.2008.01.013
Dove, G., Halskov, K., Forlizzi, J., & Zimmerman, J. (2017). UX Design Innovation: Challenges for Working with Machine Learning as a Design Material. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (May 6–11, 2017), Denver, CO, USA, 278–288. https://doi.org/10.1145/3025453.3025739
Gawlick, R. (2016). Methodological Aspects of Qualitative-Quantitative Analysis of Decision-Making Processes. Management and Production Engineering Review, 7(2), 3–11. https://doi.org/10.1515/mper-2016-0011
Hu, K. (2023, February 2). ChatGPT sets record for fastest-growing user base – analyst note. Reuters. Retrieved from https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9
Liew, A. (2007). Understanding Data, Information, Knowledge and Their Inter-Relationships. Journal of Knowledge Management Practice, 7(2). Retrieved from https://www.researchgate.net/publication/224937037_Understanding_Data_Information_Knowledge_And_Their_Inter-Relationships
Lipitakis, E. A. (Ed.). (1993). Advances on Computer Mathematics and Its Applications. World Scientific Publishing Company.
Lipitakis, A., & Lipitakis, E. A. E. C. (2014). E-business Performance and Strategy Planning E-Valuation Based on Adaptive Algorithmic Modelling Methods: Critical Factors Affecting E-Valuation and Strategic Management Methodologies. Universal Journal of Management, 2(2), 81–91. https://doi.org/10.13189/ujm.2014.020204
Lipitakis, A., & Phillips, P. (2016). On e-business strategy planning and performance: a comparative study of the UK and Greece. Technology Analysis & Strategic Management, 28(9), 266–289. https://doi.org/10.1080/09537325.2015.1094568
McCall, J. (2005). Genetic algorithms for modelling and optimization. Journal of Computational and Applied Mathematics, 184(1), 205–222. https://doi.org/10.1016/j.cam.2004.07.034
Paschek, D., Luminosu, C. T., & Draghici, A. (2017). Automated business process management – in times of digital transformation using machine learning or artificial intelligence. 8th International Conference on Manufacturing Science and Education – MSE 2017 “Trends in New Industrial Revolution”, 121, Art. 04007. https://doi.org/10.1051/matecconf/201712104007
Pinheiro, L., & Dras, M. (2017). Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading. 6–15. Retrieved from https://aclanthology.org/U17-1001.pdf
Rattan, P., Penrice, D. D., & Simonetto, D. A. (2022). Artificial Intelligence and Machine Learning: What You Always Wanted to Know but Were Afraid to Ask. Gastro Hep Advances, 1, 70–78. https://doi.org/10.1016/j.gastha.2021.11.001
Ritue, J. (2010). Vice President of IDC Technology Spotlighting. Acceleration and Operationalize AI Deployments Using AI-Optimized Infrastructure.
Zhang, J., Zhan, Z. H., Lin, Y., Chen, Y., Gong, Y., Chung, H. S. H., & Li, Y. (2011). Evolutionary Computation Meets Machine Learning: A Survey. Computational Intelligence Magazine, IEEE, 6(4), 68–75. https://doi.org/10.1109/MCI.2011.942584
Zohuri, B., & Moghaddam, M. (2017). Neural Network Driven Artificial Intelligence: Decision Making Based on Fuzzy Logic (Series: Computer Science, Technology and Applications: Mathematics Research Developments). Nova Science Publishers.

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