CONSTRUCTION OF SORTING ALGORITHMS

Authors

  • O. Makarov
  • V. Shynkarenko

DOI:

https://doi.org/10.34185/1991-7848.itmm.2025.01.055

Keywords:

constructive-synthesizing modeling, software, information technology, algorithm, formal grammars, genetic algorithm.

Abstract

With the development of digital technologies and the increase in the volume of processed data, the efficiency of sorting algorithms is becoming critical. The paper considers the evolution of sorting algorithms from classical to hybrid methods, in particular Timsort and Introsort, which demonstrate improved time characteristics and stability compared to traditional approaches. Special attention is paid to data preprocessing methods and their impact on performance. A constructive-synthesizing modeling approach is proposed to create adaptive sorting algorithms, which allows combining existing methods and forming new effective algorithms. The use of a genetic algorithm in the design process allows automating the selection of optimal sorting strategies according to the characteristics of the input data. The results obtained confirm the prospects of using a constructive-synthesizing approach to build adaptive sorting algorithms that provide high performance in various conditions.

References

Peters T. Timsort description, accessed june 2015. http://svn.python.org/projects/python/trunk/Objects/listsort.txt.

David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley. – 1989. p. 412.

Shinkarenko V. I., Makarov O. V. Study of the Efficiency of Some Certain Deterministic Preprocessing Methods for Sorting Algorithms. Problems of Programming – 2023. No. 4. pp. 3–14. doi: 10.15407/pp2023.04.003

Shinkarenko V. I., Doroshenko A. Yu., Yatsenko O. A., Raznosilin V. V., Galanin K. K., Data Stochastic Preprocessing for Sorting Algorithms, CEUR in: Proceedings of the 13th International Scientific and Practical Programming Conference UkrPROG 2022, vol. 3501, 29- 38.

Marceau, Robert R. An Analysis of the Worst-Case Performance of Quicksort. Rivier Academic Journal – 2011. № 7.1.

Havrylenko S. Yu. Formal Languages, Grammars, and Automata. Kharkiv: NTU «KhPI» – 2021. p. 133.

Shinkarenko V. I., Makarov O. V. Structural Adaptation of Sorting Algorithms Based on Constructive Fragments. CEUR-WS. – 2024. № 3806. p. 16-29.

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Published

2025-06-04

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