• Аndrii Oliinyk
  • Ievgen Fedorchenko
  • Vasyl Donenko
  • Аlexandеr Stepanenko
  • Serhii Korniienko
  • Anastasia Kharchenko


genetic algorithm, evolutionary algorithm, financial indicators optimization, C#, SQL Server Analysis Services, minimization of stock in warehouse time


The work is devoted to the problem of optimizing the financial performance of pharmacies. To solve this problem, a genetic method of multicriteria optimization was developed with a mutation operator modification to study the degree of influence of factors on the financial performance of pharmacies and the choice of optimization model. The fundamental difference between the developed genetic algorithm and its existing counterparts is the ability to control the mathematical distribution of the values of the solution, which prevents premature convergence of the genetic algorithm and uses all proposed genes in fractions according to the chosen distribution model. A comparative analysis of the work of classical GA and modified versions shows that the best results are achieved in the cognitive-style determination. Three modifications of the mutation operator were developed. The application of the developed methods will lead to a more effective use of the pharmacy area, to reduce unmet demand and, ultimately, to reduce the retail cost of drugs by reducing the costs of storing and servicing the suboptimal loading of the pharmacy.


H. Sak, A. Senior and F. Beaufays, Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition, Cornell University Library, 2014.

On some extensions to GA package, Cornell University Library, 2017, [Online]. Available at:

K. Swingler, Applying Neural Networks: A Practical Guide, Morgan Kaufman Publishers, 2001, 301 p.

D.S. Broomhead, “Radial basis functions, multi-variable functional interpolation and adaptive networks,” RSRE Memorandum, no. 4148, pp. 35, 1988.

J.J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proc. of the Natl. Acad. Sci. USA, vol. 79, pp. 2554 – 2558, 1982.

F. Rosenblatt, “The perceptron: a probabilistic model for information storage and organization in the brain,” Cornell Aeronautical Laboratory, vol. 65, no. 6, pp. 386–408, 1958.

H. Bourlard and Y. Kamp, “Auto-association by multilayer perceptrons and singular value decomposition,” Biologycal Cybernetics, vol. 59, pp. 291–294, 1988.

C. Renfro, M. Patti, J. Ballou and S. Ferreri, “Development of a medication synchronization common language for community pharmacies,” Journal of the American Pharmacists Association, vol. 58, no. 5, pp. 515-521, 2018.

M. Livet and J. Easter, “Optimizing medication use through a synergistic technology testing process integrating implementation science to drive effectiveness and facilitate scale,” Journal of the American Pharmacists Association, vol. 59, pp. S71-S77, 2019.

Q. Zhang, D. Wang and Y. Wang, “Convergence of decomposition methods for support vector machines,” Neurocomputing, vol. 317, pp. 179-187, 2018.

M. Gen and R. Cheng, Genetic Algorithms and Engineering Design, John Wiley & Sons, 1997, 352 p.

S. Haupt and R. Haupt, Practical Genetic Algorithms, Wiley, 2004, 261 p.

Willach Pharmacy Solutions – UK, 2018, [Online]. Available at:

M. Adibi, “Single and multiple outputs decision tree classification using bi-level discrete-continues genetic algorithm,” Pattern Recognition Letters, vol. 128, pp. 190-196, 2019.

A.O. Oliinyk, O.O. Oliinyk, S.A. Subbotin, “Software-hardware systems: Agent technologies for feature selection,” Cybernetics and Systems Analysis, no. 48(2), pp. 257-267, 2012. DOI: 10.1007/s10559-012-9405-z.

A. Oliinyk, I. Fedorchenko, A. Stepanenko, M. Rud and D. Goncharenko, “Evolutionary method for solving the traveling salesman problem,” Proceedings of the 2018 5th International Scientific-Practical Conference (PICST), Kharkiv: Kharkiv National University of Radioelectronics, 2018, pp. 331–339.

I. Fedorchenko, A. Oliinyk, A. Stepanenko, T. Zaiko, S. Korniienko, N. Burtsev, “Development of a genetic algorithm for placing power supply sources in a distributed electric network,” Eastern European Journal of Enterprise Technologies, issue 5/3 (101), pp. 6–16, 2019. DOI: 10.15587/1729-4061.2019.180897.

J. R. Dopico, J. D. de la Calle and A. P. Sierra, “Encyclopedia of artificial intelligence,” New York: Information Science Reference, vol. 1-3, pp. 1677, 2009.

Y. Nagata and S. Kobayashi, “A powerful genetic algorithm using edge assembly crossover for the traveling salesman problem,” INFORMS Journal on Computing, vol. 25, no. 2, pp. 346-363, 2013.

K. Hoffman, M. Padberg and G. Rinaldi, “Traveling salesman problem,” Encyclopedia of Operations Research and Management Science, pp. 1573-1578, 2013.

Y. Wang, “The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem,” Computers & Industrial Engineering, vol. 70, pp. 124-133, 2014.

D. Sanches, D. Whitley and R. Tinos, “Improving an exact solver for the traveling salesman problem using partition crossover,” Proceedings of the Genetic and Evolutionary Computation Conference GECCO’17, 2017, pp. 344-337.

A. Hussain, Y. Muhammad, M. Nauman Sajid, I. Hussain, A. Mohamd Shoukry and S. Gani, “Genetic algorithm for traveling salesman problem with modified cycle crossover operator,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1-7, 2017.

C. Tsai, S. Tseng, M. Chiang, C. Yang and T. Hong, “A high-performance genetic algorithm: Using traveling salesman problem as a case,” The Scientific World Journal, vol. 2014, pp. 1-14, 2014.

M. Lapan, Deep Reinforcement Learning Hands-on, Packt Publishing, 2018, 546 p.

A. Oliinyk, T. Zaiko, S. Subbotin, “Training sample reduction based on association rules for neuro-fuzzy networks synthesis,” Optical Memory and Neural Networks (Information Optics), vol. 23, issue 2, pp. 89-95, 2014. DOI: 10.3103/S1060992X14020039

B. Mei and Y. Xu, “Multi-task least squares twin support vector machine for classification,” Neurocomputing, vol. 338, pp. 26-33, 2019.

K. Ghazvini, M. Yousefi, F. Firoozeh and S. Mansouri, “Predictors of tuberculosis: Application of a logistic regression model,” Gene Reports, vol. 17, p. 100527, 2019.

N. Buduma and N. Locascio, Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, O'Reilly Media, 2017, 298 p.

T. Bittner, “A limitation with least squares predictions,” Teaching Statistics, vol. 35, no. 2, pp. 80-83, 2012.

D. Dutta, J. Sil and P. Dutta, “Automatic clustering by multi-objective genetic algorithm with numeric and categorical features,” Expert Systems with Applications, vol. 137, pp. 357-379, 2019.

D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, pp. 432, 1989.

J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975, 232 p.

L. Chambers, The Practical Handbook of Genetic Algorithms, CRC Press, vol. I, 2000, 520 p.

L. Chambers, The Practical Handbook of Genetic Algorithms, CRC Press, vol. II, 2000, 421 p.

L. Chambers, The Practical Handbook of Genetic Algorithms, CRC Press, vol. III, 2000, 659 p.

E. Cantu-Paz, Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers, 2001, 162 p.

B. Lin, X. Sun, S. Salous, “Solving travelling salesman problem with an improved hybrid genetic algorithm,” Journal of Computer and Communications, vol. 4, pp. 98-06, 2016.

G. Baloch and F. Gzara, “Capacity and assortment planning under one-way supplier-driven substitution for pharmacy kiosks with low drug demand,” European Journal of Operational Research, vol. 282, pp. 108-128, 2020.




How to Cite

Oliinyk А., Fedorchenko, I., Donenko, V., Stepanenko А., Korniienko, S., & Kharchenko, A. (2020). DEVELOPMENT OF AN EVOLUTIONARY OPTIMIZATION METHOD FOR FINANCIAL INDICATORS OF PHARMACIES. International Journal of Computing, 19(3), 449-463. Retrieved from