Evaluating premature convergence for metaheuristic.
Premature convergence is a common problem to population based metaheurustic. The evaluation of premature convergence rate is difficult to obtain because the stochastic nature of metaheuristic. This paper presents a statistical effort to evaluate and predict the premature rate and performance of meta...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
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Serials Publications
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/14584/1/Evaluating%20premature%20convergence%20for%20metaheuristic.pdf |
| _version_ | 1825945455940337664 |
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| author | Md Sultan, Abu Bakar Abdullah, Azizol Mahmod, Ramlan Abdullah @ Selimun, Mohd Taufik |
| author_facet | Md Sultan, Abu Bakar Abdullah, Azizol Mahmod, Ramlan Abdullah @ Selimun, Mohd Taufik |
| author_sort | Md Sultan, Abu Bakar |
| collection | UPM |
| description | Premature convergence is a common problem to population based metaheurustic. The evaluation of premature convergence rate is difficult to obtain because the stochastic nature of metaheuristic. This paper presents a statistical effort to evaluate and predict the premature rate and performance of metaheuristic. The Fitness Distance Correlation technique was used to determine the premature rate and the memetic algorithm is tested on five selected timetabling datasets. The results shows that using relatively less effort, we can gain meaningful values of premature problems. |
| first_indexed | 2024-03-06T07:31:48Z |
| format | Article |
| id | upm.eprints-14584 |
| institution | Universiti Putra Malaysia |
| language | English English |
| last_indexed | 2024-03-06T07:31:48Z |
| publishDate | 2008 |
| publisher | Serials Publications |
| record_format | dspace |
| spelling | upm.eprints-145842015-10-23T08:12:50Z http://psasir.upm.edu.my/id/eprint/14584/ Evaluating premature convergence for metaheuristic. Md Sultan, Abu Bakar Abdullah, Azizol Mahmod, Ramlan Abdullah @ Selimun, Mohd Taufik Premature convergence is a common problem to population based metaheurustic. The evaluation of premature convergence rate is difficult to obtain because the stochastic nature of metaheuristic. This paper presents a statistical effort to evaluate and predict the premature rate and performance of metaheuristic. The Fitness Distance Correlation technique was used to determine the premature rate and the memetic algorithm is tested on five selected timetabling datasets. The results shows that using relatively less effort, we can gain meaningful values of premature problems. Serials Publications 2008 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/14584/1/Evaluating%20premature%20convergence%20for%20metaheuristic.pdf Md Sultan, Abu Bakar and Abdullah, Azizol and Mahmod, Ramlan and Abdullah @ Selimun, Mohd Taufik (2008) Evaluating premature convergence for metaheuristic. International Journal of Computer Science and Engineering System, 2 (3). pp. 187-188. ISSN 0973-4406 English |
| spellingShingle | Md Sultan, Abu Bakar Abdullah, Azizol Mahmod, Ramlan Abdullah @ Selimun, Mohd Taufik Evaluating premature convergence for metaheuristic. |
| title | Evaluating premature convergence for metaheuristic. |
| title_full | Evaluating premature convergence for metaheuristic. |
| title_fullStr | Evaluating premature convergence for metaheuristic. |
| title_full_unstemmed | Evaluating premature convergence for metaheuristic. |
| title_short | Evaluating premature convergence for metaheuristic. |
| title_sort | evaluating premature convergence for metaheuristic |
| url | http://psasir.upm.edu.my/id/eprint/14584/1/Evaluating%20premature%20convergence%20for%20metaheuristic.pdf |
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