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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018


Asanov Y.A.   Beletskaya S.Y.   Al-saedi mohanad R.  

UDC 681.3
DOI: 10.26102/2310-6018/2020.29.2.009

  • Abstract
  • List of references
  • About authors

The aim of this work is to develop a modification of the adaptive genetic algorithm based on switching crossover in accordance with the degree of elitism of individuals in the population. Despite the enormous amount of research done in the field of evolutionary calculus in recent years, algorithms of this class today have a high prospect of modification. The main aim of research is carried out in order to improve the convergence rate of algorithms (to obtain high-performance optimization methods) and increase the accuracy of the solutions obtained. In the article, for the adaptive tuning of the crossover operator, the concepts of discrete and continuous degree of elitism of individuals are used. In addition, an elitism score is used to adjust the probability of a mutation. This modification has a serious advantage superiority in test problems which are traditionally used to analyze the efficiency of genetic algorithms. The test set used was a quadratic function with three variables, a Rosenbrock function, a step function, a complex fourth-order function with noise, and the Sheckel function. The results of comparing classical genetic algorithms with algorithms using the considered crossover and mutation tuning strategies are presented. An analysis of the results of a computational experiment is presented.

1. Hatta K, Matsuda K, Wakabayashi S, Koide T. On-the-fly crossover adaptation of genetic algorithms. Proc IEE/IEEE Genetic Algorithms in Engineering Systems: Innovations and Applications. 1997:197-202.

2. Gladkov L.A., Kureychik V.V., Kureychik V.M. Genetic algorithms: schoolbook. M.: Fizmatlit. 2006.

3. Beletskaya S.Yu., Bokovaya N.V. Optimal design technology for developing production systems. Management Systems and Information Technology. 2008;2-2(32):223-226.

4. Liles W.C., Wiegand R.P. Introduction to Schema Theory: A survey lecture of pessimistic & exact schema theory. Computer Science Department, George Mason University. 2002.

5. Hatta K. Adaptive choice of crossover type in genetic algorithms. 1998:900-909.

6. Coello Carlos. An updated survey of GA-based multiobjective optimization techniques. ACM Computing Surveys. 2000;32(2):109-143.

7. Alshraideh М., Mahafzah В. A MultiplePopulation Genetic Algorithm for Branch Coverage Test Data Generation. Software Quality Control. 2011;19(3):489-513.

8. Rashedi E., Nezamabadi-pour H., Saryazdi S. GSA: A Gravitational Search Algorithm. 2009;179(13):2232-2248.

9. Zhang C. and Wang H.-P., Mixed-discrete nonlinear optimization with simulated annealing. Engineering Optimization. 1993;21(4):277-291.

10. Xue C, Dong L, Li G. An Improved Immune Genetic Algorithm for the Optimization of Enterprise Information System based on Time Property[J]. Journal of Software. 2011;6(3):436-443.

Asanov Yuri Anatolevich

Email: asanovjura@mail.ru

Voronezh Institute of High Technologies

Voronezh, Russian Federation

Beletskaya Svetlana Yuryevna
Doctor of Technical Sciences Professor
Email: su_bel@mail.ru

Volgograd State Technical University

Voronezh, Russian Federation

Al-saedi mohanad Ridha ghanim

Volgograd State Technical University

Voronezh, Russian Federation

Keywords: genetic algorithm, switching crossover, adaptive mutation tuning, elitism, evolutionary calculus

For citation: Asanov Y.A. Beletskaya S.Y. Al-saedi mohanad R. MODIFICATION OF GENETIC ALGORITHM WITH ADAPTIVE CROSSOVER SWITCHING. Modeling, Optimization and Information Technology. 2020;8(2). Available from: https://moit.vivt.ru/wp-content/uploads/2020/05/AsanovSoavtors_2_20_1.pdf DOI: 10.26102/2310-6018/2020.29.2.009 (In Russ).


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