ABOUT FEATURES OF MUTATION APPLICATION IN A MODIFIED OPERATOR GENETIC ALGORITHM

International Academy Journal Web of Scholar

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Title ABOUT FEATURES OF MUTATION APPLICATION IN A MODIFIED OPERATOR GENETIC ALGORITHM
ABOUT FEATURES OF MUTATION APPLICATION IN A MODIFIED OPERATOR GENETIC ALGORITHM
 
Creator Leonid Oliinyk
Stanislav Bazhan
 
Subject Genetic algorithm
mutation
matrices
rossover operators
mathematical modeling
Genetic algorithm
mutation
matrices
rossover operators
mathematical modeling
 
Description Genetic algorithm is a method of optimization based on the concepts of natural selection and genetics. Genetic algorithms are used in software development, in artificial intelligence systems, a wide range of optimization problems and in other fields of knowledge.One of the important issues in the theory of genetic algorithms and their modified versions is the search for the best balance between performance and accuracy. The most difficult in this sense are problems where the fitness function in the search field has many local extremes and one global or several global extremes that coincide.The effectiveness of the genetic algorithm depends on various factors, such as the successful creation of the primary population. Also in the theory of genetic algorithms, recombination methods play an important role to obtain a better population of offspring. The aim of this work is to study some types of mutations using a modified genetic algorithm to find the minimum function of one variable.The article presents the results of research and analysis of the impact of some mutation procedures. Namely, the effect of mutation on the speed of achieving the solution of the problem of finding the global extremum of a function of one variable. For which a modified genetic algorithm is used, where the operators of the "generalized crossover" are stochastic matrices
Genetic algorithm is a method of optimization based on the concepts of natural selection and genetics. Genetic algorithms are used in software development, in artificial intelligence systems, a wide range of optimization problems and in other fields of knowledge.One of the important issues in the theory of genetic algorithms and their modified versions is the search for the best balance between performance and accuracy. The most difficult in this sense are problems where the fitness function in the search field has many local extremes and one global or several global extremes that coincide.The effectiveness of the genetic algorithm depends on various factors, such as the successful creation of the primary population. Also in the theory of genetic algorithms, recombination methods play an important role to obtain a better population of offspring. The aim of this work is to study some types of mutations using a modified genetic algorithm to find the minimum function of one variable.The article presents the results of research and analysis of the impact of some mutation procedures. Namely, the effect of mutation on the speed of achieving the solution of the problem of finding the global extremum of a function of one variable. For which a modified genetic algorithm is used, where the operators of the "generalized crossover" are stochastic matrices
 
Publisher RS Global Sp. z O.O.
 
Date 2020-12-23
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier https://rsglobal.pl/index.php/wos/article/view/1781
10.31435/rsglobal_wos/30122020/7324
 
Source International Academy Journal Web of Scholar; No 8(50) (2020): International Academy Journal Web of Scholar
International Academy Journal Web of Scholar; № 8(50) (2020): International Academy Journal Web of Scholar
2518-1688
2518-167X
 
Language eng
 
Relation https://rsglobal.pl/index.php/wos/article/view/1781/1619
 
Rights Copyright (c) 2020 The authors
https://creativecommons.org/licenses/by/4.0
 

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