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...
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| Format: | Article |
| Language: | English |
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Ferdowsi University of Mashhad
2017-04-01
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| Series: | Iranian Journal of Numerical Analysis and Optimization |
| Subjects: | |
| Online Access: | https://ijnao.um.ac.ir/article_24522_2fc0c38bd93842983946ab2276baecf0.pdf |
| _version_ | 1819139012504322048 |
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| author | leila lotfi Katooli Akbar shahsavand |
| author_facet | leila lotfi Katooli Akbar shahsavand |
| author_sort | leila lotfi Katooli |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T11:15:54Z |
| format | Article |
| id | doaj.art-418a7097c0cd458c97e236c431ba195d |
| institution | Directory Open Access Journal |
| issn | 2423-6977 2423-6969 |
| language | English |
| last_indexed | 2024-12-22T11:15:54Z |
| publishDate | 2017-04-01 |
| publisher | Ferdowsi University of Mashhad |
| record_format | Article |
| series | Iranian Journal of Numerical Analysis and Optimization |
| spelling | doaj.art-418a7097c0cd458c97e236c431ba195d2022-12-21T18:28:01ZengFerdowsi 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 |
| 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 |
| title | 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_short | A Reliable Approach for Terminating the GA Optimization Method |
| title_sort | reliable approach for terminating the ga optimization method |
| 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 |
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