Comparison of the performance of scripting and compiled languages based on the operation of the genetic algorithm
Abstract
The aim of this work was to compare the performance of selected programming languages (Python, C) by measuring the time of operation and use of computer resources of the genetic algorithm for given parameters, and then assessing whether the scripting language can be
comparable in terms of speed with the compiled language. For this purpose, a genetic algorithm has been implemented in each of these
languages and test scenarios were developed. The results form the basis for the final evaluation of the performance of the presented languages and proof that the scripting language can achieve operating times comparable to the compiled language.
Keywords:
Python; C; efficiencyReferences
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[3] https://en.wikipedia.org/wiki/C_(programming_language) [05.01.2019]
[4] Lutz M.: Learning Python. O'Reilly Media, 2013.
[5] Ateeq M., Habib H., Umer A., Rehman M. U.: C++ or Python? Which One to Begin with: A Learner's Perspective. [W]: 2014 International Conference on Teaching and Learning in Computing and Engineering, IEEE, 11-13 April 2014.
[6] Dobrescu L.: Replacing ANSI C with other modern programming languages. [W]: 2014 International Symposium on Fundamentals of Electrical Engineering (ISFEE), IEEE, 28-29 Nov. 2014.
[7] Prechelt L.: An empirical comparison of seven programming languages. Computer, Volume: 33, Issue: 10, Oct 2000.
[8] Jun L., Ling L.: Comparative research on Python speed optimization strategies. [W]: 2010 International Conference on Intelligent Computing and Integrated Systems, IEEE, 22-24 Oct. 2010.
[9] Zhang H., Nie J.: Program performance test based on different computing environment. [W]: 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), IEEE, 28-29 May 2016.
[10] Man K. F., Tang K. S., Kwong S.: Genetic algorithms: concepts and applications [in engineering design]. IEEE Transactions on Industrial Electronics, 1996, Volume: 43, Issue: 5, Oct 1996, p.: 519 - 534.
[11] Srinivas M., Patnaik L. M.: Genetic algorithms: a survey. Computer, 1994, Volume: 27, Issue: 6, June 1994, p.: 17 - 26. [12] http://psyco.sourceforge.net/ [02.02.2019]
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[16] Merelo-Guervós J. J., Blancas-Álvarez I., Castillo P. A., Romero G., Rivas V. M., García-Valdez M., Hernández-Águila A., Romáin M.: A comparison of implementations of basic evolutionary algorithm operations in different languages. [W]: 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE, 24-29 July 2016.
[17] Suman S., Giri V. K.: Genetic Algorithms: Basic Concepts and Real World Applications. Gorakhpur, Uttar Pradesh, India, May 2016.
[18] Myalapalli V. K., Myalapalli J. K., Savarapu P. R.: High performance C programming. [W]: 2015 International Conference on Pervasive Computing (ICPC), IEEE, 8-10 Jan. 2015.
[19] Gerardo de la Fraga L., Tlelo-Cuautle E., Azucena A. D. P.: On
the Execution Time of a Computational Intensive Application
in Scripting Languages. [W]: 2017 5th International Conference
in Software Engineering Research and Innovation
(CONISOFT), IEEE, 25-27 Oct. 2017.
Dzikowski, F. (2019). Comparison of the performance of scripting and compiled languages based on the operation of the genetic algorithm. Journal of Computer Sciences Institute, 11, 137–144. https://doi.org/10.35784/jcsi.178
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