Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms
Self-adaptation in evolutionary computation refers to the encoding of parameters into tire chromosome to allow for the self-organization process to act on the parameters in addition to the design variables. This paper investigates the feasibility of introducing a self-adaptive mutation operator into...
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| Format: | Conference or Workshop Item |
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2006
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| _version_ | 1825713127262519296 |
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| author | Teo, Jason Tze Wi |
| author_facet | Teo, Jason Tze Wi |
| author_sort | Teo, Jason Tze Wi |
| collection | UMS |
| description | Self-adaptation in evolutionary computation refers to the encoding of parameters into tire chromosome to allow for the self-organization process to act on the parameters in addition to the design variables. This paper investigates the feasibility of introducing a self-adaptive mutation operator into a real-coded evolutionary algorithm called the Generalized Generation Gap (G3) algorithm. G3 is currently one of the most efficient as well as effective state-of-the-art real-coded genetic algorithms (RCGAs) but the drawback is that its performance on multimodal optimization problems is known to be poor compared to unimodal optimization problems. In this research, our objective is to introduce a self-adaptive mutation operator into G3, of which the mutation decision parameter is evolved along with the search variables during the evolutionary optimization process. The proposed algorithm is tested using four well-known multimodal benchmark test problems with many local optima surrounding their global optimum. It was found that the performance of the modified G3 algorithm with self-adaptive mutation outperformed the original G3 algorithm in two out of the four test problems in terms of the solution precision achieved. |
| first_indexed | 2025-03-05T00:48:38Z |
| format | Conference or Workshop Item |
| id | ums.eprints-1066 |
| institution | Universiti Malaysia Sabah |
| last_indexed | 2025-03-05T00:48:38Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | ums.eprints-10662014-12-29T08:24:31Z https://eprints.ums.edu.my/id/eprint/1066/ Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms Teo, Jason Tze Wi QA75.5-76.95 Electronic computers. Computer science Self-adaptation in evolutionary computation refers to the encoding of parameters into tire chromosome to allow for the self-organization process to act on the parameters in addition to the design variables. This paper investigates the feasibility of introducing a self-adaptive mutation operator into a real-coded evolutionary algorithm called the Generalized Generation Gap (G3) algorithm. G3 is currently one of the most efficient as well as effective state-of-the-art real-coded genetic algorithms (RCGAs) but the drawback is that its performance on multimodal optimization problems is known to be poor compared to unimodal optimization problems. In this research, our objective is to introduce a self-adaptive mutation operator into G3, of which the mutation decision parameter is evolved along with the search variables during the evolutionary optimization process. The proposed algorithm is tested using four well-known multimodal benchmark test problems with many local optima surrounding their global optimum. It was found that the performance of the modified G3 algorithm with self-adaptive mutation outperformed the original G3 algorithm in two out of the four test problems in terms of the solution precision achieved. 2006 Conference or Workshop Item PeerReviewed Teo, Jason Tze Wi (2006) Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms. In: International Conference on Computing and Informatics, 06-08 Jun 2006 , Kuala Lumpur, Malaysia. http://dx.doi.org/10.1109/ICOCI.2006.5276440 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Teo, Jason Tze Wi Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms |
| title | Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms |
| title_full | Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms |
| title_fullStr | Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms |
| title_full_unstemmed | Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms |
| title_short | Self-adaptive mutation for enhancing evolutionary search in real-coded genetic algorithms |
| title_sort | self adaptive mutation for enhancing evolutionary search in real coded genetic algorithms |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| work_keys_str_mv | AT teojasontzewi selfadaptivemutationforenhancingevolutionarysearchinrealcodedgeneticalgorithms |