DETERMINING THE SIZE OF THE POPULATION OF THE GENETIC ALGORITHM FOR THE PROBLEMS OF DISCRETE OPTIMIZATION IN CAD
https://doi.org/10.21122/2309-4923-2018-3-9-16
Abstract
A method for determining the size of a population is proposed. The general approach for determining the size of a population follows from the statement that the chromosomes of a population must contain the maximum number of different values that cover most of the search area. The method is based on the regression model, which allows you to determine the size of the population, depending on the permissible number of values of the independent variable. The regression model is obtained as a result of processing simulation data in the formation of a population for a single-variable objective function. The problem is solved for a genetic algorithm, where the genotype is represented by a chromosome in binary code, and the phenotype by a decimal integer code of values of independent variables. This allows you to model the formation of a population without reference to specific values of variables. The model was obtained for the power range of the reference sets from 12 to 52, and allows to predict the size of the population beyond the limits of this range. The main area of use of this method is discrete optimization problems with objective functions of several variables, where the ranges of admissible values are finite and have a small dimension.
About the Author
V. V. Frolov
V. N. Karazin Kharkiv National University
Ukraine
D. Sc., Associate Professor, Professor of the Department of Theoretical and Applied Informatics
Ukraine
D. Sc., Associate Professor, Professor of the Department of Theoretical and Applied Informatics
References
1. Emel’janov V. V., Kurejchik V. V., Kurejchik V. M. Teorija i praktika jevoljucionno-go modelirovanija. – M.: FIZMATLIT, 2003. – 432 s.
2. Coj Ju. R. Nejrojevoljucionnyj algoritm i programmnye sredstva dlja obrabotki izobrazhenij. [Jelektronnyj resurs]: dis. na soiskanie uchenoj stepeni kand. tehn. nauk: spec. 05.13.01 – Sistemnyj analiz, upravlenie i obrabotka informacii (otrasl’: informacija i informacionnye sistemy) / Ju. R Coj. – Tomsk, 2007. – 209 s. – Rezhim dostupa: http://qai.narod.ru/Dissertation/tsoy_phd.thesis.zip (data obrashhenija: 27.12.2017).
3. Chipiga A. F., Petrov Ju. Ju. Ocenka vybora razmera populjacii v prostom geneti-cheskom algoritme. [Jelektronnyj resurs] // Informacionnoe protivodejstvie ugrozam terrorizma. – 2005 g. – № 5. – S. 26–29. – Rezhim dostupa: https://elibrary.ru/item.asp?id9572194 (data obrashhenija: 23.03.2018).
4. Semjonychev E. V., Kurkin E. I., Danilova A. A. Vybor parametrov genetiche-skih algoritmov v zadachah parametricheskoj identifi nelinejnyh modelej dina-miki // Vestnik samarskogo municipal’nogo instituta upravlenija. – 2013 g. – № 1(24). – S. 130–140. – Rezhim dostupa: https://elibrary.ru/download/elibrary_19024835_95315377.pdf (data obrashhenija: 27.12.2017).
5. Zherebcova O. V., Zub’jak D. R. Geneticheskij algoritm i ego svojstva dlja reshenija zadachi poiska pustoj prjamougol’noj oblasti maksimal’noj ploshhadi // Vestnik Volzh-skogo universiteta im. V. N. Tatishheva. – 2011 g. – № 17. – S. 160–164.– Rezhim dostupa: https://elibrary.ru/item.asp?id= 16858033 (data obrashhenija: 23.03.2018).
6. Muhamedieva D. T. Analiz osobennostej geneticheskih algoritmov // Problemy vychislitel’noj i prikladnoj matematiki. – 2015 g. – № 1(1). – Rezhim dostupa: https:// elibrary.ru/item.asp? id= 25625477 (data obrashhenija: 23.03.2018).
7. Saprykin A. N., Akinina K. D., Saprykina E. N. Nahozhdenie optimal’nogo chisla poleznyh osobej v populjacii i konvergiruemyh pokolenij geneticheskogo algoritma op-timizacii prostyh mnogojekstremal’nyh funkcij // ACTUALSCIENCE. – 2016 g. – № 11. – Tom 2. – S.168–169. – Rezhim dostupa: https://elibrary.ru/item.asp?id27542766 (data obra-shhenija: 23.03.2018).
8. Saprykin A. N., Akinina K. D. Osnovnye harakteristiki geneticheskogo algorit-ma komponovki blokov jelektronno-vychislitel’nyh sredstv // Informacionnye tehnolo-gii, mezhvuzovskij sbornik nauchnyh trudov. – Rjazan’: Rjazanskij gosudarstvennyj radio-tehnicheskij universitet. – 2017. – S. 128–132. – Rezhim dostupa: https://elibrary.ru/item.asp?id=30470701 (data obrashhenija: 24.03.2018).
9. Saprykin A. N., Akinina K. D., Saprykina E. N. Optimal’noe chislo konvergiru-emyh pokolenij i poleznyh osobej pri var’irujushhihsja znachenijah verojatnosti mutacii geneticheskogo algoritma na malyh prostranstvah poiska // Sovremennye tehnologii v nauke i obrazovanii – STNO-2017 Sbornik trudov II mezhdunarodnoj nauchno-tehnicheskoj i nauchno-metodicheskoj konferencii Tom. 4. – Rjazan’: Rjazanskij gosudar-stvennyj radiotehnicheskij universitet. – 2017. – S. 248–252. – Rezhim dostupa: https://elibrary.ru/item.asp?id=29875353 (data obrashhenija: 24.03.2018).
10. Fedorenko K. V., Olovjannikov A. L. Issledovanie osnovnyh parametrov geneti-cheskogo algoritma primenitel’no k zadache poiska optimal’nogo marshruta // Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makaro-va. – 2017. – T. 9. – № 4. – S. 714–723. – Rezhim dostupa: https://elibrary.ru/item.asp id=29922476 (data obrashhenija: 24.03.2018).
11. Frolov V. V. Osobennosti realizacii geneticheskogo algoritma dlja proektiro-vanija tehnologicheskih sistem mehanicheskoj obrabotki // Vostochno-Evropejskij zhurnal peredovyh tehnologij. – 2012. – № 3/9(57). – S.60–64.
Review
For citations:
Frolov V.V. DETERMINING THE SIZE OF THE POPULATION OF THE GENETIC ALGORITHM FOR THE PROBLEMS OF DISCRETE OPTIMIZATION IN CAD. «System analysis and applied information science». 2018;(3):9-16. (In Russ.) https://doi.org/10.21122/2309-4923-2018-3-9-16
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