Crossover and mutation operators of genetic algorithms
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in...
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| Language: | en |
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International Association of Computer Science and Information Technology
2017
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| Online Access: | http://eprints.uthm.edu.my/3688/1/AJ%202017%20%28515%29.pdf http://eprints.uthm.edu.my/3688/ https://dx.doi.org/10.18178/ijmlc.2017.7.1.611 |
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| author | Siew, Mooi Lim Md. Sultan, Abu Bakar Sulaiman, Md. Nasir Mustapha, Aida Leong, K. Y. |
| author_facet | Siew, Mooi Lim Md. Sultan, Abu Bakar Sulaiman, Md. Nasir Mustapha, Aida Leong, K. Y. |
| author_sort | Siew, Mooi Lim |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole. |
| format | Article |
| id | my.uthm.eprints-3688 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2017 |
| publisher | International Association of Computer Science and Information Technology |
| record_format | eprints |
| spelling | my.uthm.eprints-36882021-11-21T07:11:49Z http://eprints.uthm.edu.my/3688/ Crossover and mutation operators of genetic algorithms Siew, Mooi Lim Md. Sultan, Abu Bakar Sulaiman, Md. Nasir Mustapha, Aida Leong, K. Y. QA75 Electronic computers. Computer science Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole. International Association of Computer Science and Information Technology 2017-02 Article PeerReviewed text en http://eprints.uthm.edu.my/3688/1/AJ%202017%20%28515%29.pdf Siew, Mooi Lim and Md. Sultan, Abu Bakar and Sulaiman, Md. Nasir and Mustapha, Aida and Leong, K. Y. (2017) Crossover and mutation operators of genetic algorithms. International Journal of Machine Learning and Computing, 7 (1). pp. 9-12. ISSN 2010-3700 https://dx.doi.org/10.18178/ijmlc.2017.7.1.611 |
| spellingShingle | QA75 Electronic computers. Computer science Siew, Mooi Lim Md. Sultan, Abu Bakar Sulaiman, Md. Nasir Mustapha, Aida Leong, K. Y. Crossover and mutation operators of genetic algorithms |
| title | Crossover and mutation operators of genetic algorithms |
| title_full | Crossover and mutation operators of genetic algorithms |
| title_fullStr | Crossover and mutation operators of genetic algorithms |
| title_full_unstemmed | Crossover and mutation operators of genetic algorithms |
| title_short | Crossover and mutation operators of genetic algorithms |
| title_sort | crossover and mutation operators of genetic algorithms |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.uthm.edu.my/3688/1/AJ%202017%20%28515%29.pdf http://eprints.uthm.edu.my/3688/ https://dx.doi.org/10.18178/ijmlc.2017.7.1.611 |
| url_provider | http://eprints.uthm.edu.my/ |