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Domański Czesław (University of Lodz, Poland), Kubacki Robert (University of Lodz, Poland)
Application of the Differential Evolution Algorithm to Group A Bank's Individual Clients
Argumenta Oeconomica Cracoviensia, 2020, no 1(22), s. 9-20, tab., bibliogr. 10 poz.
Bankowość, Metody grupowania, Algorytmy
Banking, Grouping methods, Algorithms
JEL Classification: C38, M31, G21.
summ., streszcz.
Objective: The aim of the article is to present the results of grouping individual clients of a bank with the differential evolution algorithm. Research Design & Methods: The research offers conclusions based on analysis of the bank's customer base and deductive and inductive reasoning.

Findings: The results of the authors' research show that the differential evolution algorithm correctly groups bank customers and can be used for this purpose.

Implications/Recommendations: The differential evolution algorithm is an alternative to the commonly used k-means algorithm. The algorithm generates several competing solutions in one iteration. It enables independence from starting vectors and greater effectiveness in searching for an optimal solution. The differential evolution algorithm was itself enriched with a variable that allows the optimal number of clusters to be selected. Each iteration contained proposed solutions (chromosomes) that were evaluated by the target function built on the CS measure proposed by Chou.

Contribution: The article presents the application of the differential evolution algorithm to group a bank's clients. (original abstract)
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  1. Chou, C. H., Su, M. C. and Lai, E. (2004) "A New Cluster Validity Measure and Its Application to Image Compression". Pattern Analysis and Applications 7 (2): 205-20,
  2. Das, S., Abraham, A. and Konar, A. (2008) "Automatic Clustering Using an Improved Differential Evolution Algorithm". IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 38 (1): 218-37, TSMCA.2007.909595.
  3. Das, S., Abraham, A. and Konar, A. (2009) Metaheuristic Clustering, vol. 178. Springer.
  4. Das, S., Mullick, S. S. and Suganthan, P. N. (2016) "Recent Advances in Differential Evolution - an Updated Survey". Swarm and Evolutionary Computation 27: 1-30,
  5. Everitt, B. S., Landau, S., Leese, M. and Stahl, D. (2011) Cluster Analysis: Wiley Series in Probability and Statistics. Wiley.
  6. Feoktistov, V. and Janaqi, S. (2004) "New Strategies in Differential Evolution" in Adaptive Computing in Design and Manufacture VI. London: Springer, pp. 335-46.
  7. Gan, G., Ma, C. and Wu, J. (2007) Data Clustering: Theory, Algorithms, and Applications, vol. 20. Siam.
  8. Giridhar, S., Notestein, D., Ramamurthy, S. and Wagle, L. (2011) "Od złożoności do orientacji na klienta". Executive Report, IBM Global Business Services.
  9. Storn, R. (1995) "Differential Evolution - a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces". Technical Report 11. International Computer Science Institute.
  10. Storn, R. and Price, K. (1997) "Differential Evolution - a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces". Journal of Global Optimization 11 (4): 341-59.
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