Neuromesh Universal Genetic Optimizer in Typical Business Decision Algorithms

Authors

  • Andrii Shkitov
  • Petro Talanchuk
  • Oleksii Zivenko
  • Anatolii Tymoshenko
  • Volodymyr Pavlenko
  • Vitalia Kropyvnytska

Keywords:

genetic algorithm, universal optimizer, neural networks, optimization, business decisions, elitism, benchmark functions, parameter tuning

Abstract

This research presents the development of a universal genetic optimizer (UGO) aimed at solving optimization problems across a wide range of test functions, focusing on achieving reliable global extrema for both theoretical and practical applications. The study utilizes a genetic algorithm (GA) enhanced with neural network-based approaches, implementing key genetic operators such as crossover, mutation, and elitism. The algorithm was tested on benchmark functions including Rosenbrock, De Jong, and Griewank, among others, with statistical analysis identifying optimal parameter settings. The results demonstrate superior performance compared to traditional tools like Excel Solver and GAMS, particularly when elitism is included. The proposed framework highlights the adaptability of evolutionary computation when combined with machine learning techniques. Moreover, it opens perspectives for applying UGO in real-world optimization challenges such as logistics, energy systems, and engineering design.

References

S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Boston, MA, USA: Pearson, 2021.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

S. Robotko, A. Topalov, S. Nekrasov, V. Zaitsev, and D. Zaitsev, “Machine learning and modeling of the impact of trademark filings on GDP growth based on Python,” Proceedings of the 5th Int. Workshop Modern Mach. Learn. Technol. Data Sci. (MoMLeT+DS 2023), Lviv, Ukraine, Jun. 3, 2023, pp. 65–76. [Online]. Available at: https://ceur-ws.org/Vol-3426/paper6.pdf.

Y. Zheng, J. Wang, S. Ryzhkov, O. Nechai, A. Topalov, O. Zivenko, S. Babchuk, and T. Harasymiv, “Computer system for forecasting water quality parameters based on machine learning,” Comptes Rendus de L'Academie Bulgare des Sciences, vol. 77, no. 11, pp. 1629–1638, 2024. https://doi.org/10.7546/CRABS.2024.11.06.

S. Robotko, A. Topalov, V. Lukashova, V. Zaytsev, and D. Zaytsev, “A machine learning study on world economic impact of intellectual property rights on GDP growth with a focus on Poland,” Proceedings of the 13th Int. Conf. Adv. Comput. Inf. Technol. (ACIT), Wrocław, Poland, 2023, pp. 261–264, https://doi.org/10.1109/ACIT58437.2023.10275475.

Y. Davidor, “Epistasis variance: A viewpoint on GA-hardness,” in Foundations of Genetic Algorithms, G. J. E. Rawlins, Ed. San Mateo, CA, USA: Morgan Kaufmann, 1991, pp. 23–35. https://doi.org/10.1016/B978-0-08-050684-5.50005-7.

K. De Jong, An Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D. dissertation, Dept. Comput. Commun. Sci., Univ. Michigan, Ann Arbor, MI, USA, 1975.

D. E. Goldberg, “Simple genetic algorithms and the minimal, deceptive problem,” Genetic Algorithms and Simulated Annealing, L. Davis, Ed. London, U.K.: Pitman, 1987, pp. 74–88.

TechStartups, “NeuroMesh: Spearheading the new era of AI with a distributed training protocol,” TechStartups, Apr. 9, 2024. [Online]. Available at: https://techstartups.com/2024/04/09/neuromesh-spearheading-the-new-era-of-ai-with-a-distributed-training-protocol/.

Y. Wang, Q. Li, and S. Chen, “Using neural-genetic hybrid systems for complex decision support,” Neural Comput. Appl., vol. 35, pp. 11403–11416, 2023, https://doi.org/10.1007/s00521-023-08305-6.

R. Sivakumar and R. Dhanasekaran, “Applying soft computing methods in business analytics using hybrid genetic algorithms,” ICTACT J. Soft Comput., vol. 15, no. 2, pp. 3539–3544, 2023. https://doi.org/10.21917/ijsc.2024.0493.

Contino, “Optimising business processes: The Darwinian way to better decision making,” Contino Insights. [Online]. Available at: https://www.contino.io/insights/optimising-business-processes-using-genetic-algorithms.

D. Karaboga and B. Akay, “Selecting an optimal architecture of neural network using genetic algorithm,” Procedia Comput. Sci., vol. 187, pp. 252–258, 2021, https://doi.org/10.1016/j.procs.2021.04.055.

X. Liu, Y. Zhang, and J. Wang, “Application of genetic algorithm and BP neural network in supply chain financing,” Eur. J. Oper. Res., vol. 284, no. 3, pp. 1074–1083, 2020, [Online]. Available at: https://www.sciencedirect.com/science/article/pii/S0377042720304611.

A. Borisenko, P Kegel, S Gorlatch, “Optimal design of multi-product batch plants using a parallel branch-and-bound method,” Proceedings of the International Conference on Parallel Computing Technologies, 2011, pp. 417-430. https://doi.org/10.1007/978-3-642-23178-0_36.

A. Borisenko, S. Gorlatch, “Comparing GPU-parallelized metaheuristics to branch-and-bound for batch plants optimization,” The Journal of Supercomputing, vol. 75, issue 12, pp. 7921-7933. 2019. https://doi.org/10.1007/s11227-018-2472-9.

M. A. Al-Betar, M. A. Awadallah, I. A. Doush, K. Alyass, O. Alqaisia, “A comprehensive review of genetic algorithms: operators, analyses, and applications,” Applied Soft Computing, vol. 97, 106721, 2020.

Y. Al-Sahou, M. A. Al-Betar, “A review of genetic algorithm components and applications,” Archives of Computational Methods in Engineering, vol. 29, issue 7, pp. 4165–4198, 2022.

R. Poli, W. B. Langdon, N. F. McPhee, “A field guide to genetic programming,” Lulu Enterprises. 2008.

K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons. 2001.

A. H. Gandomi, A. H. Alavi, “Cuckoo search algorithm: a review of the literature and its applications,” Journal of Applied Soft Computing, vol. 12, issue 12, pp. 4338–4348, 2012.

A. P. Piotrowski, J. J. Napiorkowski, “Performance of evolutionary algorithms on benchmark functions,” Entropy, vol. 22, issue 8, 856, 2020.

M. Črepinšek, M. Mernik, S. Liu, “Exploitation and exploration in evolutionary algorithms: A survey,” ACM Computing Surveys (CSUR), vol. 45, issue 3, pp. 1–33, 2013. https://doi.org/10.1145/2480741.2480752.

J. Dréo, P. Siarry, A. Petrowski, E. Taillard. Metaheuristics for Hard Optimization: Methods and Case Studies, Springer. 2006.

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Published

2026-03-31

How to Cite

Shkitov, A., Talanchuk, P., Zivenko, O., Tymoshenko, A., Pavlenko, V., & Kropyvnytska, V. (2026). Neuromesh Universal Genetic Optimizer in Typical Business Decision Algorithms. International Journal of Computing, 25(1), 140-148. Retrieved from https://www.computingonline.net/computing/article/view/4498

Issue

2026, Volume 25, Issue 1

Section

Articles

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