A Reliable Approach for Terminating the GA Optimization Method

Genetic algorithm (GA) has been extensively used in recent decades to solve many optimization problems in various fields of science and engineering. In most cases, the number of iterations is the only criterion which is used to stop the GA. In practice, this criterion will lead to prolong execution t...

Full description

Bibliographic Details
Main Authors: leila lotfi Katooli, Akbar shahsavand
Format: Article
Language:English
Published: Ferdowsi University of Mashhad 2017-04-01
Series:Iranian Journal of Numerical Analysis and Optimization
Subjects:
Online Access:https://ijnao.um.ac.ir/article_24522_2fc0c38bd93842983946ab2276baecf0.pdf
id doaj-418a7097c0cd458c97e236c431ba195d
record_format Article
spelling doaj-418a7097c0cd458c97e236c431ba195d2021-06-02T05:55:39ZengFerdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772423-69692017-04-01718310610.22067/ijnao.v7i1.4865224522A Reliable Approach for Terminating the GA Optimization Methodleila lotfi Katooli0Akbar shahsavand1Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, IranDepartment of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, IranGenetic algorithm (GA) has been extensively used in recent decades to solve many optimization problems in various fields of science and engineering. In most cases, the number of iterations is the only criterion which is used to stop the GA. In practice, this criterion will lead to prolong execution times to ensure proper solution. A novel approach is presented in this article as the approximate number of decisive iterations (ANDI ) which can be used to successfully terminate the GA optimization method with minimum execution time. Two simple correlations are presented which relate the new parameter (ANDI ) with approximate degrees of freedom (Adf ) of the merit function at hand. For complex merit functions, a linear smoother (such as Regularization network) can be used to estimate the required Adf. Four illustrative case studies are used to successfully validate the proposed approach by effectively finding the optimum point by using to the presented correlation. The linear correlation is more preferable because it is much simpler to use and the horizontal axis represents the approximate (not exact) degrees of freedom. It was also clearly shown that the Regularization Networks can successfully filter out the noise and mimic the true hyper-surface underlying a bunch of noisy data set.https://ijnao.um.ac.ir/article_24522_2fc0c38bd93842983946ab2276baecf0.pdfgenetic algorithmtermination criterionapproximate degrees of freedomapproximate number of decisive iterationlinear smoother con- ceptregularization networks
collection DOAJ
language English
format Article
sources DOAJ
author leila lotfi Katooli
Akbar shahsavand
spellingShingle leila lotfi Katooli
Akbar shahsavand
A Reliable Approach for Terminating the GA Optimization Method
Iranian Journal of Numerical Analysis and Optimization
genetic algorithm
termination criterion
approximate degrees of freedom
approximate number of decisive iteration
linear smoother con- cept
regularization networks
author_facet leila lotfi Katooli
Akbar shahsavand
author_sort leila lotfi Katooli
title A Reliable Approach for Terminating the GA Optimization Method
title_short A Reliable Approach for Terminating the GA Optimization Method
title_full A Reliable Approach for Terminating the GA Optimization Method
title_fullStr A Reliable Approach for Terminating the GA Optimization Method
title_full_unstemmed A Reliable Approach for Terminating the GA Optimization Method
title_sort reliable approach for terminating the ga optimization method
publisher Ferdowsi University of Mashhad
series Iranian Journal of Numerical Analysis and Optimization
issn 2423-6977
2423-6969
publishDate 2017-04-01
description Genetic algorithm (GA) has been extensively used in recent decades to solve many optimization problems in various fields of science and engineering. In most cases, the number of iterations is the only criterion which is used to stop the GA. In practice, this criterion will lead to prolong execution times to ensure proper solution. A novel approach is presented in this article as the approximate number of decisive iterations (ANDI ) which can be used to successfully terminate the GA optimization method with minimum execution time. Two simple correlations are presented which relate the new parameter (ANDI ) with approximate degrees of freedom (Adf ) of the merit function at hand. For complex merit functions, a linear smoother (such as Regularization network) can be used to estimate the required Adf. Four illustrative case studies are used to successfully validate the proposed approach by effectively finding the optimum point by using to the presented correlation. The linear correlation is more preferable because it is much simpler to use and the horizontal axis represents the approximate (not exact) degrees of freedom. It was also clearly shown that the Regularization Networks can successfully filter out the noise and mimic the true hyper-surface underlying a bunch of noisy data set.
topic genetic algorithm
termination criterion
approximate degrees of freedom
approximate number of decisive iteration
linear smoother con- cept
regularization networks
url https://ijnao.um.ac.ir/article_24522_2fc0c38bd93842983946ab2276baecf0.pdf
work_keys_str_mv AT leilalotfikatooli areliableapproachforterminatingthegaoptimizationmethod
AT akbarshahsavand areliableapproachforterminatingthegaoptimizationmethod
AT leilalotfikatooli reliableapproachforterminatingthegaoptimizationmethod
AT akbarshahsavand reliableapproachforterminatingthegaoptimizationmethod
_version_ 1721407923782942720